Perkembangan teknologi digital mengalami perkembangan yang sangat pesat dan memberikan berbagai kemudahan dalam kehidupan. Salah satu diantaranya adalah pembuatan video untuk berbagai macam kebutuhan dan kepentingan. Pada saat ini, banyak terjadi pelanggaran hak cipta yang dilakukan oleh orang yang tidak bertanggung jawab seperti membajak video dengan cara merekam video tersebut. Watermarking adalah salah satu teknik yang dapat digunakan untuk melindungi hak cipta atas data multimedia dengan cara menyisipkan informasi ke dalam data multimedia tersebut. Pada Tugas Akhir ini, digunakan watermark berupa citra biner dengan ukuran 64×64 piksel, dan data video host berformat AVI dengan resolusi Full High Definition FHD yang berdurasi 10 detik dengan frame rate 30 fps. Pertama, dilakukan transformasi Dual-Tree Complex Wavelet Transform DT-CWT untuk dekomposisi video host yang akan disisipi watermark. Kemudian dilakukan proses penyisipan watermark ke dalam data video. Proses watermarking video menggunakan metode Dual-Tree Complex Wavelet Transform DT-CWT menghasilkan keluaran yaitu watermarked video, yang selanjutnya akan diberikan serangan digicam menggunakan kamera jenis mirrorless yang akan mengganggu sinyal. Setelah diberikan gangguan digicam, akan dilakukan proses ekstraksi untuk memisahkan watermark dan video host. Penelitian ini menghasilkan parameter terbaik pada level 1 dan pohon 1 DTCWT, layer V pada struktur YUV, subband HH, bagian real, dengan citra watermark 64×64 piksel, dan nilai penguat ? 40. Data video menghasilkan nilai rata-rata PSNR 39,2863 dB, dan BER 0,78% pada saat tanpa serangan serta BER 17,9473 % setelah diserang dengan serangan digicam. Kata Kunci Watermarking, Video Watermark, Dual-Tree Complex Wavelet Transform, Pseudorandom Watermarking, Digicam

Digital watermarking is the technique used to embed author's credentials, logo or some other information into digital images which can be used in authentications for courtroom evidence, copyright claims and other applications. The objective of this work is to develop a feasible and invisible watermark embedding hardware for the secure digital cameras using LeGall 5/3 Discrete Wavelet Transform DWT. Bind watermarking tecnique is proposed here. The proposed architecture considers constraints of digital camera such as area, speed, power, robustness and invisibility. The algorithm is evaluated under the attacks like JPEG Joint Photographic Experts Group compression, noise, scaling and rotation to verify robustness and invisibility properties. Watermarking processor is described using Verilog HDL and synthesized using μm technology UMC standard cell library for VLSI implementation. To read the full-text of this research, you can request a copy directly from the authors.... DWT based implementation needs to store results at each level of computation, so the memory requirement increases. This is one of the reasons for higher area requirement compared to DCT based approaches [10]. ...... An authentication digital camera is a camera with built-in copyright protection and security mechanism for images produced by it. [10,19,20] have presented various secure digital camera models. ...... In [10] DWT based implementation is used to develop a feasible and invisible watermark embedding hardware for the secure digital camera. The proposed scheme of the secure watermarking has described using Verilog HDL, and synthesized using technology UMS standard cell library for VLSI implementation. ...Mustafa Osman Rameshwar RaoThe increasing amount of applications using digital multimedia technologies has accentuated the need to provide copyright protection to multimedia data. This paper reviews watermarking techniques, by focusing on the hardware based implementation of digital image watermarking. Digital watermarking is an authentication method that has received a lot of attention in the past few years. Through this paper we will survey some digital image watermarking schemes which have been implemented by using hardware techniques. Also the study shows the similarities and differences between different types and then assesses the benefits gained from the use of this technology.... A method for field programmable gate array FPGA and System-on-Chip SoC implementation is part of this research. In [11], the authors demonstrate a hardware capable of an invisible watermark embedding with the LeGall 5/3 Discrete Wavelet Transform DWT. The suggested structural design addresses the limitations of standard digital cameras. ...Smart Healthcare is envisioned as the combination of traditional healthcare augmented by smart bio-sensors, wearable devices and a plethora of on-body sensors that communicate with smart hospitals, smart emergency response systems, and ambulances, through advanced information and communication technologies. The vision of smart healthcare as part of a smart city relies on the framework of the Internet of Things IoT as the underlying core technology that enables the design and operation of a city, whereby smart technology, energy grids, transportation, buildings, communication, and information technology, are all interconnected. The present paper address some of the challenges faced in the IoT infrastructure, specifically secure communication and user authentication in the context of automated analysis of biomedical images and communication of the analysis results and related metadata in a smart healthcare framework. A hardware architecture for a Secure Digital Camera SDC integrated with the Secure Better Portable Graphics SBPG compression algorithm, suitable for applications in the IoT, is proposed in this paper. The focus of this work is on patient data protection and authentication. The proposed SBPG architecture offers two layers of protection concurrent encryption and watermarking which address all issues related to security, privacy, and digital rights management DRM. The experimental results demonstrate that the new compression technique BPG outperforms JPEG in terms of compression quality and compressed file size while providing increased image quality. High performance requirements of BPG have been met by employing two techniques 1 insertion of an encrypted signature in the center portion of the image, and 2 frequency domain watermarking using block-wise DCT of size 8×8 pixels. These approaches optimize the proposed architecture by decreasing computational complexity while maintaining strong protection, with concomitant increase of the speed of the watermarking and compression processes. A Simulink prototype for the proposed architecture has been built and tested. To the best of the authors’ knowledge, the hardware architecture for BPG compression with built-in image authentication capability for integration with a secure digital camera is the first one ever proposed.... It also provides a method for field programmable gate array FPGA implementation. Darji et al. [2] show hardware capable of entrenching invisible watermark using LeGall 5/3 Discrete Wavelet Transform DWT. In [3], a novel scheme is introduced to support pictures and illustrations captured by digital cameras. ...This paper proposes a hardware architecture for a Secure Digital Camera SDC integrated with Secure Better Portable Graphics SBPG compression algorithm. The proposed architecture is suitable for high performance imaging in the Internet of Things IoT. The objectives of this paper are twofold. On the one hand, the proposed SBPG architecture offers double-layer protection encryption and watermarking. On the other hand, the paper proposes SDC integrated with secure BPG compression for real time intelligent traffic surveillance ITS. The experimental results prove that the new compression technique BPG outperforms JPEG in terms of compression quality and size of the compression file. As the visual quality of the watermarked and compressed images improves with larger values of PSNR, the results show that the proposed SBPG substantially increases the quality of the watermarked compressed images. To achieve a high performance architecture three techniques are considered first, using the center portion of the image to insert the encrypted signature. Second, watermarking is done in the frequency domain using block-wise DCT size 8×8. Third, in BPG encoder, the proposed architecture uses inter and intra prediction to reduce the temporal and spatial redundancy.... Its compatibility was also assessed with different multi-media constructing electrical devices, and system-on-achip SoC technology is a central component of the model. Darji et al. [17] show the development of hardware capable of entrenching invisible watermarks using a LeGall 5/3 Discrete Wavelet Transform DWT. In the suggested structural design, the authors have considered all the limitations of a digital camera. ...Image or video exchange over the Internet of Things IoT is a requirement in diverse applications, including smart health care, smart structures, and smart transportations. This paper presents a modular and extensible quadrotor architecture and its specific prototyping for automatic tracking applications. The architecture is extensible and based on off-the-shelf components for easy system prototyping. A target tracking and acquisition application is presented in detail to demonstrate the power and flexibility of the proposed design. Complete design details of the platform are also presented. The designed module implements the basic proportional–integral–derivative control and a custom target acquisition algorithm. Details of the sliding-window-based algorithm are also presented. This algorithm performs $20times $ faster than comparable approaches in OpenCV with equal accuracy. Additional modules can be integrated for more complex applications, such as search-and-rescue, automatic object tracking, and traffic congestion analysis. A hardware architecture for the newly introduced Better Portable Graphics BPG compression algorithm is also introduced in the framework of the extensible quadrotor architecture. Since its introduction in 1987, the Joint Photographic Experts Group JPEG graphics format has been the de facto choice for image compression. However, the new compression technique BPG outperforms the JPEG in terms of compression quality and size of the compressed file. The objective is to present a hardware architecture for enhanced real-time compression of the image. Finally, a prototyping platform of a hardware architecture for a secure digital camera SDC integrated with the secure BPG SBPG compression algorithm is presented. The proposed architecture is suitable for high-performance imaging in the IoT and is prototyped in Simulink. To the best of our- knowledge, this is the first ever proposed hardware architecture for SBPG compression integrated with an embeddings are fundamentally a form of word representation that links the human understanding of knowledge meaningfully to the understanding of a machine. The representations can be a set of real numbers a vector. Word embeddings are scattered depiction of a text in an n-dimensional space, which tries to capture the word meanings. This paper aims to provide an overview of the different types of word embedding techniques. It is found from the review that there exist three dominant word embeddings namely, Traditional word embedding, Static word embedding, and Contextualized word embedding. BERT is a bidirectional transformer-based Contextualized word embedding which is more efficient as it can be pre-trained and fine-tuned. As a future scope, this word embedding along with the neural network models can be used to increase the model accuracy and it excels in sentiment classification, text classification, next sentence prediction, and other Natural Language Processing tasks. Some of the open issues are also discussed and future research scope for the improvement of word encoding complexity of an image format is a vigorously updating area of study in the field of two-layer protection with wavelet transform compression. In the proposed method, hybrid 2D-FDCT watermarking and RSA encryption for multispectral images predicted an efficient system. This approach satisfies the encryption security, robustness and classification accuracy retention of an algorithm. The two-layer protection of encrypted and embedded watermark image followed by wavelet transform compression minimizes the file size in the exhaustive process for encoding. An important merit is that encoding time is very much reduced in contrast to other security and compression mechanisms. The enhanced value of PSNR as well as trade-off of MES, normalized cross-correlation, the average difference and structural content improves the storage large file size medical image and improves bandwidth to an acceptable level. Priya DhinaMamatha G SKeratoconus detection and diagnosis has become a crucial step of primary importance in the preoperative evaluation for the refractive surgery. With the ophthalmology knowledge improvement and technological advancement in detection and diagnosis, artificial intelligence AI technologies like machine learning ML and deep learning DL play an important role. Keratoconus being a progressive disease leads to visual acuity and visual quality. The real challenge lies in acquiring unbiased dataset to predict and train the deep learning models. Deep learning plays a very crucial role in upturning ophthalmology division. Detecting early stage keratoconus is a real challenge. Hence, our work aims to primarily focus on detecting an early stage and multiple classes of keratoconus disease using deep learning models. This review paper highlights the comprehensive elucidation of machine learning and deep learning models used in keratoconus detection. The research gaps are also identified from which to obtain the need of the hour for detecting keratoconus in humans even before the symptoms are Pendyala Aniket GokhaleThis paper utilizes a spatial domain Watermarking algorithm applicable on grayscale images. Spatial domain technique is utilized taking advantage of it low computational complexity. The initial stage of approach is accomplished by building the algorithm on MATLAB R2014a© platform and then shifting the base to ISE Design Suite platform. The VLSI implementation of the spatial domain Watermarking algorithm is targeted on device xc5vlx50t-1ff1136 of Virtex-5 family. The robustness of the Watermarking algorithm is verified by attacking the Watermarked image with various types of noise, compression, transformation and geometrical attacks. The application of Multiple Watermarking technique also renders the effectiveness and robustness of the watermarking this paper, 2D integer wavelet transform based watermarking is carried out for the grayscale image with its VLSI architectural implementations. In the 2D integer wavelet transformation the lifting scheme is adopted and the watermarking operation is carried out in the LL2 frequency subbands. The entire watermark embedding process and extraction process are modeled in MATLAB and analyzed against the signal processing attacks like compression, salt & pepper noise, rotation and Intensity transformation attacks. Finally the same algorithm is modeled using Verilog HDL and implemented using ALTERA paper proposes a new spatial domain watermarking of grayscale images and has also shown its VLSI Implementation without altering its content in real time using a secret key. The secret key is generated by searching the watermark pixel values in host image content and the location maps are marked in secret key. Therefore this algorithm is called PVSA- Pixel Value Search Algorithm. The proposed algorithm does not make any change in the host image. Thus it shows high robustness to signal processing attacks. The watermark extraction process is simple as the host content is extracted based on key. We have evaluated the robustness of the algorithm against several signal processing attacks using MATLAB. Finally we have implemented the same algorithm in verilog HDL using Altera is the process that embeds data called a watermark, tag or label into a multimedia object, such as images, video or text for their copyright protection. According to human perception, the digital watermarks can either be visible or invisible. A visible watermark is a secondary translucent image overlaid into the primary image and appears visible to a viewer on a careful inspection. The invisible watermark is embedded in such a way that the modifications made to the pixel value is perceptually not noticed and it can be recovered only with an appropriate decoding mechanism. In this paper, we present a new VLSI architecture for implementing two visible digital image watermarking schemes. The proposed architecture is designed aiming at easy integration into any existing digital camera framework. To our knowledge, this is the first VLSI architecture for implementing visible watermarking schemes. A prototype chip consisting of 28469 gates is implemented using 035" technology, which consumes 69mW power while operating at 292MHz. Saraju P. MohantyRenuka Kumara CSridhara NayakBoth encryption and digital watermarking techniques need to be in- corporated in a digital rights management framework to address different aspects of content management. While encryption transforms original multimedia ob- ject into another form, digital watermarking leaves the original object intact and recognizable. The objective is to develop low power, real time, reliable and se- cure watermarking systems, which can be achieved through hardware implemen- tations. In this paper, we present an FPGA based implementation of an invisi- ble spatial domain watermarking encoder. The watermarking encoder consists of a watermark generator, watermark insertion module, and a controller. Most of the invisible watermarking algorithms available in the literature and also the al- gorithm implemented in this paper insert pseudorandom numbers to host data. Therefore, we focus on the structural design aspects of watermarking generator using linear feedback shift register. We synthesized the prototype watermarking encoder chip using Xilinx this brief, we present a new VLSI architecture that can insert invisible or visible watermarks in images in the discrete cosine transform domain. The proposed architecture incorporates low-power techniques such as dual voltage, dual frequency, and clock gating to reduce the power consumption and exploits pipelining and parallelism extensively in order to achieve high performance. The supply voltage level and the operating frequency are chosen for each module so as to maintain the required bandwidth and throughput match among the different modules. A prototype VLSI chip was designed and verified using various Cadence and Synopsys tools based on TSMC technology with M transistors and mW of estimated dynamic is the process that embeds data called a watermark, a tag, or a label into a multimedia object, such as images, video, or text, for their copyright protection. According to human perception, the digital watermarks can either be visible or invisible. A visible watermark is a secondary translucent image overlaid into the primary image and appears visible to a viewer on a careful inspection. The invisible watermark is embedded in such a way that the modifications made to the pixel value is perceptually not noticed, and it can be recovered only with an appropriate decoding mechanism. This paper presents a new very large scale integration VLSI architecture for implementing two visible digital image watermarking schemes. The proposed architecture is designed to aim at easy integration into any existing digital camera framework. To the authors' knowledge, this is the first VLSI architecture for implementing visible watermarking schemes. A prototype chip consisting of 28 469 gates is implemented using mu/m technology, which consumes power while operating at 292 this paper, we propose an architecture that performs the forward and inverse discrete wavelet transform DWT using a lifting-based scheme for the set of seven filters proposed in JPEG2000. The architecture consists of two row processors, two column processors, and two memory modules. Each processor contains two adders, one multiplier, and one shifter. The precision of the multipliers and adders has been determined using extensive simulation. Each memory module consists of four banks in order to support the high computational bandwidth. The architecture has been designed to generate an output every cycle for the JPEG2000 default filters. The schedules have been generated by hand and the corresponding timings listed. Finally, the architecture has been implemented in behavioral VHDL. The estimated area of the proposed architecture in technology is mm square, and the estimated frequency of operation is 200 Mhz. Ingrid DaubechiesWim SweldensThis article is essentially tutorial in nature. We show how any discrete wavelet transform or two band subband filtering with finite filters can be decomposed into a finite sequence of simple filtering steps, which we call lifting steps but that are also known as ladder structures. This decomposition corresponds to a factorization of the polyphase matrix of the wavelet or subband filters into elementary matrices. That such a factorization is possible is well-known to algebraists land expressed by the formula SLn; R[z, z-1] = En; R[z, z-1]; it is also used in linear systems theory in the electrical engineering community. We present here a self-contained derivation, building the decomposition from basic principles such as the Euclidean algorithm, with a focus on applying it to wavelet filtering. This factorization provides an alternative for the lattice factorization, with the advantage that it can also be used in the biorthogonal, non-unitary case. Like the lattice factorization, the decomposition presented here asymptotically reduces the computational complexity of the transform by a factor two. Ir has other applications, such as the possibility of defining a wavelet-like transform that maps integers to integers. Ingrid DaubechiesWim SweldensThis paper is essentially tutorial in nature. We show how any discrete wavelet transform or two band subband ltering with nite lters can be decomposed into a nite sequence of simple lter - ing steps, which we call lifting steps but that are also known as ladder structures. This decomposition corresponds to a factorization of the polyphase matrix of the wavelet or subband lters into elementary matrices. That such a factorization is possible is well-known to algebraists and expressed by the formula ; it is also used in linear systems theory in the electrical engineering community. We present here a self-contained derivation, building the decomposition from basic principles such as the Euclidean algorithm, with a focus on applying it to wavelet ltering. This factorization provides an alternative for the lattice factorization, with the advantage that it can also be used in the biorthogonal, non-unitary case. Like the lattice factorization, the decomposition presented here asymptotically re- duces the computational complexity of the transform by a factor two. It has other applications, such as the possibility of dening a wavelet-like transform that maps integers to HuangChangsheng YangWatermarking is a technique for labeling digital picture by hiding secret information in the images. This paper presents a method of watermark embedding and extracting based on discrete wavelet transform of blocks and Arnold transform. Different with most previous work, which uses a random number of a sequence of bits as a watermark, the proposed method embeds a watermark with visual recognizable patterns, such as gray image in images. In the proposed method, each pixel of watermark is embedded in the wavelet coefficient of the middle and low frequency of a block in the images. Unlike other watermarking techniques that use a single casting energy, this method casts watermarks in multi-energy level. The performance of the proposed watermarking is robust to variety of signal distortions, such a JPEG, image cropping, sharpening, and blurring ChenJeanne ChenJian-Guo ChenIn this paper we propose an effective watermark scheme for embedding and extracting based on the JPEG2000 Codec process. Our embedding algorithm applies the torus automorphisms TA technique to break up and scramble a watermark. The scrambled watermark was embedded into the quantized bitstreams of JPEG2000 before the entropy coding stage. Distortion reduction DR was applied to the compressed image to lessen image degradation caused by the embedding process. Our watermark scheme is simple and easy to implement. Furthermore, it is robust to attacks like blurring, edge enhancement, and other image processing Lim Soonyoung ParkSeong-Jun KangWan-Hyun ChoIn this paper, we present an FPGA implementation of a watermarking-based authentication algorithm for a digital camera to authenticate the snapshots in a manner that any changes of contents in the still image will be reflected in the embedded watermark. All components of a digital camera and a watermark algorithm are implemented in VHDL, simulated, synthesized and loaded into an FPGA device. To achieve the semifragile characteristics that survive a certain amount of compression, we employ the property of DCT coefficients quantization proposed by Lin and Chang 2000. The binary watermark bits are generated by exclusive ORing the binary logo with pseudo random binary sequence. Then watermark bits are embedded into the LSBs of DCT coefficients in the medium frequency range. The system consists of three main parts image capture and LCD controller, watermark embedding part, and camera control unit. The FPGA implemented digital camera is tested to analyze the performance. It is shown that the watermarking algorithm can embed the watermark into the original image coming from a sensor much faster than the software implementation and the embedded image is easily transmitted to the PC by using the USB interface. The quality of the transmitted image is also comparable to the one implemented by a software SatyanarayanaR. Satish Kumar Udipi NiranjanDigital watermarking is a technique of embedding imperceptible information into digital documents. In this paper, a VLSI implementation of the digital watermarking technique is presented for 8 bit gray scale images. This implementation of fragile invisible watermarking is carried out in the spatial domain. The standard ASIC design flow for a μm CMOS technology has been used to implement the algorithm. The area of the chip is 3453×3453 μm2 and the power consumption is lifting based 1-D discrete wavelet transform DWT core is proposed. It is re-configurable for 5/3 and 9/7 filters in JPEG2000. Folded architecture is adopted to reduce the hardware cost and achieve the higher hardware utilization. Multiplication is realized in hardwired multiplier with coefficients represented in canonic signed-digit CSD form. It is a compact and efficient DWT core for the hardware implementation of JPEG2000 encoderJames L. MannosDavid J. SakrisonShannon's rate-distortion function provides a potentially useful lower bound against which to compare the rate-versus-distortion performance of practical encoding-transmission systems. However, this bound is not applicable unless one can arrive at a numerically-valued measure of distortion which is in reasonable correspondence with the subjective evaluation of the observer or interpreter. We have attempted to investigate this choice of distortion measure for monochrome still images. This investigation has considered a class of distortion measures for which it is possible to simulate the optimum in a rate-distortion sense encoding. Such simulation was performed at a fixed rate for various measures in the class and the results compared subjectively by observers. For several choices of transmission rate and original images, one distortion measure was fairly consistently rated as yielding the most satisfactory appearing encoded SweldensIn this paper we present the basic idea behind the lifting scheme, a new construction of biorthogonal wavelets which does not use the Fourier transform. In contrast with earlier papers we introduce lifting purely from a wavelet transform point of view and only consider the wavelet basis functions in a later stage. We show how lifting leads to a faster, fully in-place implementation of the wavelet transform. Moreover, it can be used in the construction of second generation wavelets, wavelets that are not necessarily translates and dilates of one function. A typical example of the latter are wavelets on the sphere. Keywords wavelet, biorthogonal, in-place calculation, lifting 1 Introduction At the present day it has become virtually impossible to give the definition of a "wavelet". The research field is growing so fast and novel contributions are made at such a rate that even if one manages to give a definition today, it might be obsolete tomorrow. One, very vague, way of thinking about...Design and implementation of a progressive image coding chip based on the lifting wavelet transformC C LiuY H ShiauJ M Jou

In the modern era of virtual computers over the notional environment of computer networks, the protection of influential documents is a major concern. To bring out this motto, digital watermarking with biometric features plays a crucial part. It utilizes advanced technology of cuffing data into digital media, text, image, video, or audio files. The strategy of cuffing an image inside another image by applying biometric features namely signature and fingerprint using watermarking techniques is the key purpose of this study. To accomplish this, a combined watermarking strategy consisting of Discrete Wavelet Transform, Discrete Cosine Transform, and Singular Value Decomposition DWT-DCT-SVD is projected for authentication of image that is foolproof against attacks. Here, singular values of watermark1 fingerprint and watermark2 signature are obtained by applying DWT-DCT-SVD. Affixing both the singular values of watermarks, we acquire the transformed watermark. Later, the same is applied to cover image to extract the singular values. Then we add these values to the cover image and transformed watermark to obtain a final watermarked image containing both signature and fingerprint. To upgrade the reliability, sturdiness, and originality of the image, a fusion of watermarking techniques along with dual biometric features is exhibited. The experimental results conveyed that the proposed scheme achieved an average PSNR value of about 40 dB, an average SSIM value of and an embedded watermark resilient to various attacks in the watermarked IntroductionCopyright infringement has increased as a result of the rapid blooming of cyberspace and communication technology, which has led to an exchange of digital mixed media content. The transmission of digital data across public networks like the Internet makes the protection of personal information and intellectual property rights IPR crucial in the modern day [1]. Digital watermarking is a means to get around this problem and prove ownership of digital assets that are being used ease of multimedia content distribution is due to the fast development of the internet, multimedia technologies, communication, and reproduction. Multimedia data is prone to issues such as illegal copying and distribution pirating, editing, and copyright. In order to protect the data from the above-mentioned issues, digital watermarking is encrypted sort of coding called a digital watermark is added to a signal that can handle sounds, such as audio, video, or image data. Biometric systems have been using watermarking techniques to safeguard and authenticate biometric data and improve recognition accuracy in an effort to boost the trustworthiness of self-awareness systems that can be differentiated between a legitimate person and a fraudster. An encrypted sort of coding called a digital watermark is added to a signal that can handle sounds, such as audio, video, or image data. Biometric systems have been using watermarking techniques to safeguard and authenticate biometric data and improve recognition accuracy in an effort to boost the trustworthiness of self-awareness systems that can tell the difference between a legitimate person and a proposed work briefs on how to authenticate images by embedding biometric information into a digital image using a new hybrid system that includes three different algorithms namely DWT-DCT-SVD. In the embedding process, the cover image undergoes a DWT transform which decomposes it into four subbands, namely, L-L, L-H, H-L, and H-H, where L-L denotes Low-Low, L-H denotes Low-High, H-L denotes High-Low, H-H denotes High-High. L-L subband undergoes DCT transform to obtain 4 × 4 blocks. The DCT transform mainly compresses the data or image. The SVD of a matrix is an orthogonal transform used for matrix diagonalization to obtain singular values of the watermark. Subsequently, the SVD factors of each block are modified to create the watermarked image, extracted, and then inserted into the cover image. In the process of extraction, the watermarked image is acquired and a reverse stratagem is utilized to obtain the watermark, which is the biometric refers to the automatic identification of people based on their physiological and behavioral features; two authentications based on behavioral and physiological characteristics for attaching the watermark to the cover image are applied. Measurements taken from the human body are used in physiological biometrics, such as fingerprints, iris, face, retina. The dynamic measurements used in behavioral biometrics such as signatures, voice, and keystrokes, are based on human actions. The proposed hybrid watermarking system is cooperative integration of signature and fingerprint watermarks to cover image to assure the integrity, authenticity, and confidentiality of the digital documents. The embedding procedure consists of two steps in the projected method. First, the embedding of the signature in the fingerprint is carried out to create the transformed watermark, as shown in Figure 1. The final watermark is created by embedding the cover image in this extraction procedure is split into two steps. Step 1 extract the fingerprint from the watermark that results in an extracted fingerprint. Step 2 the signature is further extracted from the extracted fingerprint image, as shown in Figure Hybrid DWT-DCT-SVDThe proposed scheme consists of DWT, DCT, and SVD for image authentication that is robust against attacks. In the process of watermarking, two major steps are carried out viz., embedding and extraction. In this, the combinations of DWT, DCT, and SVD along with their inverses are applied. This hybrid technique is suitable for different image processing attacks by achieving the properties of watermarks, integrity, authenticity, and confidentiality of digitized image documents. The performance metrics used in this research are Peak Signal to Noise Ratio PSNR, Structural Similarity Index SSIM, and Normalized Correlation NC. This proposed methodology is deployed on dual watermarking where the embedding process consists of DWT, DCT, and SVD which provide image authentication and is robust against embedding process consists of DWT, DCT, and SVD watermarking techniques. To cover the image, one level of DWT is applied. Hence applied SVD to the L-L sub-band. Besides, the application of DWT to the biometric and then DCT followed by the SVD technique is carried out. Parallelly, SVD is applied to the signature. Application of SVD to the images results in three matrices namely U S and V. Considered the singular valued S matrix as it contains the diagonal properties of the image. Further, added the singular values of the biometric and alpha times of the signature. To recreate the L-L sub-hand of biometric the inverse of the SVD is applied. Later, we applied inverse DCT as we applied DCT in the earlier steps. Now we have applied inverse DWT to create an image with a modified L-L subband. This gives a results in the transformed watermark. Now apply the application of SVD to it in order to get a singular valued matrix. Next, to cover the image, singular values are added off and beta times singular matrix of the transformed watermark. Now apply the inverse SVD to recreate the cover image with manipulated singular values. Then followed by applying DCT and then DWT to create an image with a modified L-L subband. This gives a final watermarked image; this contains the signature and biometric embedded on the cover image, and this completes the embedded process. The extraction process for the transformed watermark biometric is done by applying DWT on the final watermark to obtain four subbands. Next, apply DCT to the L-L sub-band followed by SVD to obtain singular values of final watermarked image. Later, DWT is followed by DCT and then SVD to obtain signature images from the transformed image. This completes the extraction DCTWhen digital photos are uncompressed, they require a massive quantity of storage space. For such uncompressed data to be transmitted across the network, large transmission bandwidth is required. The most common image compression method is the Discrete Cosine Transform DCT [1]. The JPEG picture compression method makes use of DCT. The two-dimensional DCT is calculated for each block of the 8 × 8 or 16 × 16 divided input image. Following that, the DCT coefficients are quantized, encoded, and DCT can store the image with only fewer coefficients, and is used in lossy image compression to reduce the redundancy between neighboring pixels. The DCT formula with a 2D matrix is shown in equation 1.where the x, yth elements of the image element are represented by the matrix p as px, y. The block’s size, N, is used for the DCT. The pixel values of the native matrix of the image equation determine the value of one entry i, jth of the modified image. For the standard JPEG 8 × 8 blocks, N = 8 and x, y is in the stretch of 0 to DCT divides pictures into components with various frequencies. Because fewer significant frequencies are dropped during quantization in the compression portion, the term lossy is in use. Later, during the decompression phase, the image is retrieved using the remaining most crucial frequencies. As a result, some distortion is included in the reconstructed images; however, the levels of distortion can be altered during the compression stage. JPEG is used for both color and black and white photographs; however, the article focuses on the DWTThe suggested methodology incorporates the Discrete Wavelet Transform DWT [2] approach to withstand the attacks with a robust model. Low-Low, Low-High, High-Low, and High-High, L-L, L-H, H-L, and H-H are four subbands created by DWT HH. The original image will be recreated using the above four subbands. The image can theoretically be processed via the filter bank as shown in Figure 3 to produce various subband frequency illustrated in Figure 4, the L-L subband defines low-pass filtering for each row and column, resulting in a low-resolution approximation of the original image. Similarly, the L-H subband was created by applying low-pass filtering to each row and high-pass filtering to each column. The L-H subband is influenced by high-frequency features along the column direction. The H-L subband is the result of high-pass and low-pass filtering on each row and column. The H-L subband is influenced by high-frequency features along the row direction. The H-H subband is created by applying high-pass filtering to each row and column. The H-H subband is influenced by high-frequency features in the diagonal direction [3].DWT-Based Feature Extraction using multilevel decomposition of previously processed pictures, DWT effectively extracts discriminant characteristics that are impervious to arbitrary environmental fluctuations. The discrete interval wavelets are sampled for the wavelet transform known as the DWT. DWT provides information about the frequency and spatial domains of a picture simultaneously. An image can be studied using the DWT operation, which combines the analysis filter bank and decimation process. A 2D transform is created from two distinct 1D transformations. In 1D DWT, the approximation coefficients hold the low-frequency information, whereas the detail coefficients hold the high-frequency information. The input image is divided into four separate subbands by the application of 2D DWT low-frequency components in the horizontal and vertical directions cA, low-frequency components in the horizontal and high-frequency components in the vertical directions cV, high-frequency components in the horizontal and low-frequency components in the vertical directions cH, and high-frequency components in the horizontal and vertical directions cD. You can alternatively write cA, cV, cH, and cD as L-L, L-H, H-L, and H-H, SVDSingular value decomposition SVD [1, 4] is a method for approximating data matrix decomposition into an optimal approximation of the signal and noise components. This is one of the most essential aspects of the SVD decomposition in noise filtering, compression, and forensics, and it can also be viewed as a properly identifiable noise refactors into three matrices for the given digital image. To refactor the image singular values are used and at the end of this process storage space required by the image is reduced as the image is represented with a smaller set of values. The SVD of M × N matrix A is given by the following equation 2.where U M × N matrix of the orthonormal eigenvectors of AAT. 𝑉𝑇 Transpose of the n × n matrix containing the orthonormal eigenvectors of A^{T}A. W N × N diagonal matrix of the singular values which are the square roots of the eigenvalues of system can be divided into a number of linearly independent components, each of which contributes its own amount of energy, using the most efficient and stable technique known as orthogonal matrix columns U are referred to as the left singular vectors, whereas the orthogonal matrix columns V are referred to as the right singular vectors. The diagonal members are reflecting the singular values of the maximum energy packing of the SVD, the ability to solve the least squares issue, the ability to compute the pseudoinverse of a matrix, and multivariate analysis are all significant benefits for images [1, 5]. A crucial characteristic of SVD is its relationship to a matrix’s rank and its capacity to approximate matrices of a particular rank. Digital images can frequently be characterized by the sum of a relatively limited number of Eigen images since they are frequently represented by low-rank matrices. Images are compressed in compression, and SVD with the highest energy packing property is typically used. As previously established, SVD divides a matrix into orthogonal parts so that the best sub-rank approximations can be made [6, 7]. Truncated SVD transformation with rank r offers significant storage savings over storing the entire matrix with acceptable quality. The block diagram for the SVD-based compression is shown in Figure illumination data can be found in the singular value matrix produced by SVD. As a result, altering the single values will directly impact how the image is illuminated. As a result, the image’s other details won’t be altered. Second, by using the L-L subband illumination enhancement, the edge information in other subbands will be protected L-H, H-L, and H-H.The study [1] the research that is being offered displays an adaptive scaling factor based on particular DWT-DCT coefficients of its image material. The role of particular DWT-DCT coefficients relative to the average value of DWT-DCT coefficients was used to construct the adaptive scaling factor. Using a suggested set of guidelines that consider the adaptive scaling factor, the watermark image was integrated. The results of the experiments showed that the suggested method produced a high PSNR value of 47 dB, an SSIM value of around and an implanted watermark resistance to many attacks in the watermarked the integration procedure in the article [5], a discrete wavelet transform is applied to the image, and then the ZigZag scanning method is used to topologically reorganize the coefficients of the L-L subbands. The watermark bits are then integrated using the resulting coefficients. The integrity of the watermark may be easily confirmed thanks to an embedded hash of the electronic patient record. The experimental results show that the approach has high invisibility with a PSNR above 70 dB and very good robustness against a wide range of geometric and destructive attacks. The invisibility and robustness of the approach have been many of the currently used hybrid SVD-based picture watermarking systems is insecure, the study [4] primarily focuses on the analysis of the state-of-the-art in this area. Additionally, there aren’t many in-depth reviews in this field. In order to draw attention to numerous security risks, unresolved challenges, and research gaps, they conducted efficiency comparisons. Based on the results, this study gives researchers and practitioners important information they can use to improve the field of picture watermarking. It also gives suggestions for how to make more reliable schemes in the work [8] achieved a superior imperceptibility of dB, and demonstrates that watermarking may be included in a host image using various transform operations, including discrete cosine transform DCT, discrete wavelet transforms DWT, and singular value decomposition SVD. But not every design criterion is met at once by a single transformation. In order to close this gap, they developed a hybrid blind digital image watermarking technique using DCT, DWT, and SVD. This method was more robust than existing state-of-the-art techniques against filter, salt-and-pepper noise SPN, and rotation attacks. The WNC value for a median filter with various window sizes is 1, which is higher than the current well-known transforms—the discrete wavelet transform DWT, discrete cosine transform DCT, and singular value decomposition—are combined in the system in [6] SVD. By reaching greater values of imperceptibility in the form of PSNR with a value of decibels dB and SSIM with a value of experimental results show that the suggested technique exceeds the strategies already published in the literature. With a maximum NCC value of and a minimum BER value of it simultaneously achieves exceptional robustness ratings. The DWT-SVD performance suggested in the study [9] was verified throughout the training phase, and the suggested system’s high invisibility and resilience against different forms of attacks on watermarked photos were also demonstrated. When the suggested system’s findings were contrasted with those of other systems, it became clear that DWT-SVD performed better against pixel-value alteration suggested work in [10] illustrates a robust watermarking technique for grayscale photos using lifting wavelet transform and singular value decomposition as the basis for multiobjective artificial bee colony optimization. Here, the actual image is changed to four subbands using three levels of lifting wavelet transform, and then the watermark image’s singular value is merged with the original image’s unique value for the L-H subband. In order to achieve the highest possible robustness without compromising watermark clarity, multiple scaling factors are used in the embedding operation on behalf of the single scaling element. The results of the experiments show that the invisibility is very good and that it is resistant to a wide range of attacks that use image processing. A non-blind watermarking NBW schemes malfunction for watermarking stratagem thereby giving out to impart perpetually imperceptibility, depriving of robustness and competence for embedding. So, to tame this drawback, an algorithm for blind watermarking BW was proposed [11] to cover the glitches of impart safeguarding of copyright that has crucial demand for color images, an image-watermarking scheme deployed on sequence-based MRT SMRT was tendered for color images [12] where the principle goal was to detect preferable color space among the habitually pre-owned color spaces. A cascaded neural network approach deployed on two different neural network models was projected [13] by using an optimized feature-based digital watermarking algorithm. Here, the cascading of the neural network spawns the potent pattern for embedding. In the study [14], researchers tendered a strategy using watermarking technique of Fourier transform for color images where image will be declined into two variants where the image is segmented into R, G and B, sections where DFT is performed and these coefficients so obtained will use medium frequency band to encapsulate [15], which comprises of discrete wave transformation technique combined with Hessenberg decomposition HD and singular value decomposition SVD using scaling factor, watermark is embedded into the cover image. In [16], a watermarking algorithm of the color image is projected, where it explores the combination of DWT-DCT-SVD. Here the host image which is in RGB space is converted to YUV color space. Then a layer of DWT is put into the luminance component Y, followed by DCT and SVD to each block. The results are good enough to embrace the attacks and imperceptibility property of watermark. In [2, 3, 7, 17], some basic comparison of watermarking with steganography and a summary of different methods of image steganography is carried out. An effective DWT–SVD is deployed with self-adaptive differential evolution SDE algorithm for image watermarking scheme, SDE adjusts the mutation factor F and the crossover rate Cr dynamically in order to balance an individual’s exploration and exploitation capability for different evolving phases to achieve invisibility [18–20]. In [21–24], comparative analysis of image compression is done by three transform methods, which are Discrete Cosine Transform DCT, Discrete Wavelet Transform DWT and Hybrid DCT + DWT Transform, thereby achieving better invisibility property and good PSNR Proposed MethodologyThis proposed methodology is deployed on dual watermarking where the embedding process consists of DWT, DCT, and SVD which provide image authentication and is robust against attacks. Figure 6 depicts the embedding process that consists of DWT, DCT, and SVD watermarking techniques. The two watermarks used in the proposed methodology are biometrics and signature. These images are converted in grayscale because the SVD can only be applied to two-dimensional images whereas the color images are of three dimensions. Since the property of DWT after one level decomposition, the host image should be larger than the watermark. For the first embedding process, biometrics is the host image and the signature is the watermark. The biometric should be larger than the signature. Here, to the cover image one level of DWT is applied. Then the image is divided into four subbands, namely, L-L, L-H, H-L, and H-H. The major details and properties of the image are stored in the L-L subband. So, we contemplate embedding the biometric into the L-L subband. So, we have applied SVD to the L-L subband. Besides we have applied DWT to the biometric and then DCT and followed by SVD. Parallelly, we applied SVD to the signature, by applying SVD to the images we obtain three matrices namely U S and proposed methodology is divided into two steps Embedding Extraction Watermark Embedding AlgorithmThe Embedding algorithm can be split into two phases process of signature into biometric Step 1 Apply SVD to the signature to obtain the singular values SVS. Step 2 Apply DWT level-1 to the biometric to obtain 4-subbands. Step 3 Apply DCT to L-L subband in order to remove redundancy. Step 4 Apply SVD to the biometric to obtain singular values SVB. Step 5 Change the singular values of biometric SVB by adding the singular values of signature SVS. Step 6 The Transformed watermark TW is obtained by applying inverse SVD, DCT and process of Transformed watermark into Cover image Step 1 Apply DWT to cover image to obtain 4-subbands. Step 2 Apply DCT to L-L subband in order to remove redundancy. Step 3 Apply SVD to obtain the singular values of cover image SVC. Step 4 Manipulate the singular values of cover image SVC by adding the singular values of transformed image SVTW. Step 5 Obtain the final watermarked image by applying the inverse of SVD, DCT, and DWT techniques on the modified Extraction ProcessFigure 7 depicts the extraction process, which is the extraction of watermarks, biometric and signature from the cover image. The extraction is carried out as follows of Transformed watermark biometric Step 1 Apply DWT on the final watermark to obtain four subbands. Step 2 Apply DCT to L-L subband in order to remove redundancy. Step 3 Apply SVD to obtain the singular values of the final watermarked image SVFW. Step 4 To obtain the transformed watermark image, subtract the singular values of final watermarked image SVFW from the cover image singular values SVC. and divide the whole with the beta of signature watermark from transformed watermark biometric Step 1 Apply DWT on transformed watermark to obtain four subbands. Step 2 Apply DCT to L-L subband in order to remove redundancy. Step 3 Apply SVD to obtain the singular values of the transformed watermark. Step 4 To obtain a signature, subtract the singular values of transformed watermark SVTM from the biometric singular values SVB. and divide the whole with the alpha Experimental ResultsThe outcome of the projected technique discloses a hybrid combination of DWT-DCT-SVD that gives the best NC values along with good PSNR and SSIM. By applying DWT alone, the host image doesn’t withstand a few attacks. So, by introducing DCT, it has the ability to pack most of the information in the fewest coefficients thereby reducing the redundancy between the neighboring pixels. By using SVD, it makes it easier to hide the image. This combination works for all sorts of attacks and also gives better Figure 8, a watermarked image of size 512 × 512 has been subjected to various watermarking attacks, including Gaussian low-pass filter, Median, Salt and Pepper noise, Speckle noise, JPEG compression, Sharpening attack, Histogram equalization, Average filter, Gaussian noise, JPEG2000 compression, and Motion blur. It was robust against all of these attacks. Figure 9 shows an extracted fingerprint of size 256 × 256. When the cover image is subjected to various watermarking attacks such as Gaussian low-pass filter, Median, Salt and Pepper noise, Speckle noise, JPEG compression, Sharpening attack, Histogram equalization, Average filter, Gaussian noise, JPEG2000 compression, and Motion blur. It is resistant to all of these Figure 10, the cover image is subjected to various watermarking attacks, such as the Gaussian low-pass filter, Median, Salt and Pepper noise, Speckle noise, JPEG compression, sharpening attack, Histogram equalization, Average filter, Gaussian noise, JPEG2000 compression, and Motion blur, an extracted signature of size 128 × 128 is displayed. It resisted all of these attacks. The graph of SSIM versus scaling factor α is shown in Figure 11. This graph depicts the behavior of SSIM values for various α values. Each line on the graph represents a different attack, such as a Gaussian low-pass filter, a Median, Salt and Pepper noise, Speckle noise, JPEG compression, sharpening attack, histogram equalization, an average filter, Gaussian noise, JPEG2000 compression, and motion graph of NC versus scaling factor α is shown in Figure 12. This graph depicts the behavior of NC values for various α values. Each line on the graph represents a different attack, such as a Gaussian low-pass filter, a median, salt and pepper noise, speckle noise, JPEG compression, sharpening attack, histogram equalization, an average filter, Gaussian noise, JPEG2000 compression, and motion blur. Figures 13a and 13b show a graph of PSNR versus different scaling factors α or β. This graph shows the behavior of PSNR values for different α or β values. A Gaussian low-pass filter, a Median, Salt and Pepper noise, Speckle noise, JPEG compression, sharpening attack, Histogram equalization, an Average filter, Gaussian noise, JPEG2000 compression, and Motion blur are all represented by lines on the graph. Figure 14 depicts graphs of NC values under various parameters subjected to various attacks. Each line in the graphs represents a different image size, such as 512 × 512, 256 × 256, and 128 × 128. The X-axis parameters are a quality factor, compression ratio, sigma, window size, variance, and strength 1- Threshold. The graph varies depending on the type of attack used.a b Table 1 shows Normalized Correlation NC values for biometric NCB and signature NCS under different types of attacks. The achieved results show better NC values for all the test cases even after the extraction of watermarks biometric and signature.Table 2 details the invisibility imperceptibility property of the watermark of the proposed watermarking scheme for different types of images. It clearly shows that the proposed algorithm for all seven images showcases an average PSNR value of and an average SSIM value of 3 depicts Peak Signal to Noise Ratio PSNR values for biometric PSNRB and signature PSNRS under different types of attacks. In the above-mentioned test cases, the results acquired are with good PSNR values even after the extraction of watermarks biometric and signature.Table 4 depicts Structural Similarity Index Metrics SSIM values for biometric SSIMB and signature SSIMS under different types of attacks. For all the above-mentioned test cases, the results achieved are with good SSIM values even after the extraction of watermarks viz, biometric, and 5 shows the NC values of various watermarked images host image where the two watermarks biometric and signature are embedded. The NC values are good enough to achieve the property of imperceptibility of both the watermarks. The table details that the proposed scheme shows comparatively good results on Lena image for crop, salt & pepper, and speckle attacks. The proposed scheme shows results on other attacks such as rotation and scaling attacks. For peppers image, the proposed scheme shows similar results to the related work [1]. It can be depicted from Table 5 that the proposed methodology DWT-DCT-SVD shows comparatively good results for all the 15 different types of attacks on Lena and Pepper ConclusionThis study extends a watermarking stratagem deployed on both transform DCT-DWT and spatial SVD domain methods. Watermarked image implementation has good PSNR, NC, and SSIM due to DCT’s energy compaction property and DWT has a better compression ratio. The results show that the proposed method besides being protective against attacks, and deployed method improves performance without sacrificing image information. The robustness of the projected watermarking strategy was assessed by performing attacks such as added noise, filtering attacks, geometrical attacks, and compression attacks. The deployed method was validated with regard to the imperceptibility of the watermarked image. The deployed method exhibits the experimental results which achieved an average PSNR of 40 dB value, an NC value of and an SSIM value of approximately In the future, more enhanced embedding techniques may be deployed to improve the standard of watermarked images meanwhile taking the flaws into account. In the future, this method can be improved by combining it with other watermarking techniques that are more conscientious and resistant to attack. The proposed method can embed a watermark into standard digital media such as audio, text, zip archives, and video, as well as holograms and 3D vector objects. This work can be expanded to conceal user data and personal AvailabilityThe dataset used for the findings can be obtained from the corresponding author upon reasonable of InterestThe authors declare that there are no conflicts of interest regarding the publication of this © 2022 Bhargavi Mokashi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

CaraSaya Memanfaatkan Fitur ScreenPad (Dual-screen) di Laptop ASUS Zenbook. Pandu Dryad-April 26, 2022. 6 Bagaimana Cara Download Video TikTok no Watermark? Juli 8, 2022. Dukung Kreatifitas Pelajar Dengan ASUS Vivobook 13 Slate OLED. Juni 30, 2022. Tips & Tutorial. Mau Beli Laptop Bekas? Perhatikan Beberapa Hal Berikut Ini! I have seen in many Android mobile phones specially Chinese brands, whenever you take a photo using the built-in stock Camera app, a “Shot on” watermark is automatically added to the bottom-left corner of the image. The watermark shows mobile phone company name and sometimes the model name as well. Many smartphone manufacturer companies apply this automatic watermark on all photos to promote their brand name and mobile phone model. In most of these mobile phones, the watermark option is enabled by default and the phone automatically puts watermark on all photos taken by the user. In some mobile phones, the watermark also shows user information such as name if set by the user. Personally I don’t like these watermarks on photos. I want a perfect shot not cluttered by watermark and phone information. Many times readers ask me how to get rid of this annoying watermark on photos shot on their mobile phones. Thankfully there is a way to disable or remove the watermark on photos in Google Android mobile phones. The Camera app allows users to show or hide watermark on photos and users can turn on or off watermark feature according to their requirements. If you are also using a smartphone and you want to disable watermark on photos taken by Camera app, this tutorial will help you. Also if your phone supports watermark feature but doesn’t add watermark automatically on photos, this tutorial will help you in adding watermarks on all photos. This tutorial will apply to all Android mobile phones which support watermark feature such as OnePlus, Xiaomi Redmi Poco, Gionee, Vivo, Oppo Realme, etc. It’ll also work on 3rd party Camera apps downloaded from Google Play Store which also support watermark feature. Check out following steps to add or remove watermarks on Camera photos in your Android mobile phones 1. First of all open Camera app in your mobile phone. 2. Now open Settings or Options in your Camera app. In some mobile phones, the Settings or Options icon Cog wheel icon is present at the top-right corner in Camera app. In some mobile phones, you need to swipe from left or bottom to access Camera Settings or Options icon. 3. Once you open Settings or Options page in Camera app, look for Watermark option. Generally its labelled as “Shot on watermark”, “Photo watermark”, “Camera watermark”, “Dual camera watermark”, etc. To disable or remove watermark on photos, set the toggle button given for watermark option to OFF. To add and show watermark on photos, set the toggle button given for watermark option to ON. That’s it. Now your mobile phone will always show or hide watermark on all photos shot by the Camera app based on the option value set by the user. Also Check [Fix] Brightness Increases to Maximum When Opening Camera App in Android Mobile Phone You are here Home » Mobiles and Internet » [Tip] Disable or Remove Watermarks on Camera Photos in Android Mobile Phones

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MenghapusFile Dalam Folder Dengan PHP 19/03/2015 Fachrul Ahaddin 16144 Read More : Download Aplikasi Sistem Informasi Surat Siap Pakai Apa Itu Cloud Server Hosting Download Gratis Sistem Informasi Surat Online Website Halo sahabat-sahabat, dalam kesempatan kali ini saya akan mencoba menciptakan baris program php buat menghapus AbstractFor authentication and copyright protection of handwritten document images, a dual watermarking algorithm that connects the robust watermarking algorithm based on Krawtchouk moments with a fragile watermarking algorithm based on MD5 hash function is presented. Hence, the robust watermarking algorithm is used to guarantee robustness by modifying frequency coefficients in Krawtchouk moments. Thus, this study proposes a fragile watermarking algorithm, which can perceive in time when the protected image is tampered. Experimental results show that the proposed algorithm can be used for copyright protection for JPEG compression attacks and tampering detection of this ReferencesArnol’d, Avez, A. Ergodic problems of classical mechanics. In The Mathematical Physics Monograph Series. W. A. Benjamin, New York 1968. E., Soria-Lorente, A. Watermarking based on Krawtchouk moments for handwritten document images. In Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. eds. IWAIPR 2018. LNCS, vol. 11047, pp. 122–129. Springer, Cham 2018. Google Scholar Chen, B., Wornell, Quantization index modulation a class of provably good methods for digital watermarking and information embedding. IEEE Trans. Inf. Theory 474, 1423–1443 2001CrossRef MathSciNet Google Scholar Fischer, A., Frinken, V., Fornés, A., Bunke, H. Transcription alignment of Latin manuscripts using hidden Markov models. In Proceedings of the 2011 Workshop on Historical Document Imaging and Processing, pp. 29–36. ACM 2011 Google Scholar Fischer, A., et al. Automatic transcription of handwritten medieval documents. In 2009 15th International Conference on Virtual Systems and Multimedia, pp. 137–142. IEEE 2009 Google Scholar Liu, Lin, Yuan, Blind dual watermarking for color images’ authentication and copyright protection. IEEE Trans. Circ. Syst. Video Technol. 285, 1047–1055 2018CrossRef Google Scholar Mohanty, Ramakrishnan, K., Kankanhalli, M. A dual watermarking technique for images. In Proceedings of the Seventh ACM International Conference on Multimedia Part 2, pp. 49–51. Citeseer 1999 Google Scholar Pastor-Pellicer, J., Afzal, Liwicki, M., Castro-Bleda, Complete system for text line extraction using convolutional neural networks and watershed transform. In 2016 12th IAPR Workshop on Document Analysis Systems DAS, pp. 30–35. IEEE 2016 Google Scholar Shivani, S., Singh, P., Agarwal, S. A dual watermarking scheme for ownership verification and pixel level authentication. In Proceedings of the 9th International Conference on Computer and Automation Engineering, pp. 131–135. ACM 2017 Google Scholar Singh, A. Robust and distortion control dual watermarking in LWT domain using DCT and error correction code for color medical image. Multimed. Tools Appl. 1–11 2019 Google Scholar Singh, Shaw, A hybrid concept of cryptography and dual watermarking LSB\\_\DCT for data security. Int. J. Inf. Secur. Priv. IJISP 121, 1–12 2018CrossRef Google Scholar Wang, N., Li, Z., Cheng, X., Chen, Y. Dual watermarking algorithm based on singular value decomposition and compressive sensing. In 2017 IEEE 17th International Conference on Communication Technology ICCT, pp. 1763–1767. IEEE 2017 Google Scholar Yap, P., Paramesran, R., Ong, Image analysis by Krawtchouk moments. IEEE Trans. Image Process. 1211, 1367–1377 2003CrossRef MathSciNet Google Scholar Download references Author informationAuthors and AffiliationsUniversidad de Granma, Carretera Central vía Holguín Km 1/2, Bayamo, Granma, CubaErnesto Avila-Domenech & Anier Soria-LorenteUniversidad Central “Marta Abreu” de Las Villas, Santa Clara, Villa Clara, CubaAlberto Taboada-CrispiAuthorsErnesto Avila-DomenechYou can also search for this author in PubMed Google ScholarAnier Soria-LorenteYou can also search for this author in PubMed Google ScholarAlberto Taboada-CrispiYou can also search for this author in PubMed Google ScholarCorresponding authorCorrespondence to Ernesto Avila-Domenech . Editor informationEditors and AffiliationsUppsala University, Uppsala, SwedenIngela NyströmUniversity of Information Science, Havana, CubaYanio Hernández HerediaUniversity of Information Science, Havana, CubaVladimir Milián Núñez Rights and permissions Copyright information© 2019 Springer Nature Switzerland AG About this paperCite this paperAvila-Domenech, E., Soria-Lorente, A., Taboada-Crispi, A. 2019. Dual Watermarking for Handwritten Document Image Authentication and Copyright Protection for JPEG Compression Attacks. In Nyström, I., Hernández Heredia, Y., Milián Núñez, V. eds Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2019. Lecture Notes in Computer Science, vol 11896. Springer, Cham. 22 October 2019 Publisher Name Springer, Cham Print ISBN 978-3-030-33903-6 Online ISBN 978-3-030-33904-3eBook Packages Computer ScienceComputer Science R0 .

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arti watermark dual camera