Stereo Vision Based Digital Watermarking Using Regularized Feature-based Disparity Estimation

Prior research studies have shown that the peak signal to noise ratio (PSNR) is the most frequent watermarked image quality metric that is used for determining the levels of strength and weakness of watermarking algorithms. Conversely, normalised cross correlation (NCC) is the most common metric used after attacks were applied to a watermarked image to verify the strength of the algorithm used. Many researchers have used these approaches to evaluate their algorithms. These strategies have been used for a long time, however, which unfortunately limits the value of PSNR and NCC in reflecting the strength and weakness of the watermarking algorithms. This paper considers this issue to determine the threshold values of these two parameters in reflecting the amount of strength and weakness of the watermarking algorithms. We used our novel watermarking technique for embedding four watermarks in intermediate significant bits (ISB) of six image files one-by-one through replacing the image pixels with new pixels and, at the same time, keeping the new pixels very close to the original pixels. This approach gains an improved robustness based on the PSNR and NCC values that were gathered. A neural network model was built that uses the image quality metrics (PSNR and NCC) values obtained from the watermarking of six grey-scale images that use ISB as the desired output and that are trained for each watermarked image’s PSNR and NCC. The neural network predicts the watermarked image’s PSNR together with NCC after the attacks when a portion of the output of the same or different types of image quality metrics (PSNR and NCC) are obtained. The results indicate that the NCC metric fluctuates before the PSNR values deteriorate.

Protection of copyright is a crucial issue in the data transmission and distribution. Considering the fact that there are different types of information available for misuse and manipulation by all the internet users it is necessary to establish the rules for copyright protection of a certain content. In this paper, special attention is given to the technical aspects of copyright protection. We took a digital image and a digital watermark into consideration. The original image has undergone a discrete wavelet transformation. Then, spread spectrum techniques were applied to the watermark. Digital watermark is then embedded into the original image. The aim of the paper is to present the comparison of results obtained using two different spread spectrum techniques in image watermarking.

In this digital era, illegitimate redistribution and protection of digital multimedia have to turn out to be the critical issue. Therefore, digital watermarking has been introduced for avoiding illegitimate works and also to ensure security and authentication as well. “Digital video watermarking is a method to hide some kind of data like audio, image, text into digital video sequences which is nothing but orders of successive still images.” This paper intends to formulate a novel video watermarking framework that includes three stages, such as (i) optimal video frame prediction, (ii) watermark embedding process and (iii) watermark extraction process. Very first, the optimal frames are determined using a new hybrid algorithm termed trial-based update on Jaya plus firefly (TU-JF) algorithm, in such a way that the peak signal-to-noise ratio (PSNR) should be maximal. The frames are assigned with a label of one or zero, where label one denotes the frame with better PSNR (can select for embedding process) and label zero denotes the frame with reduced PSNR (cannot be used for embedding). Consequently, a data library is formed from the obtained results, where each frame of videos is determined with their gray-level co-occurrence matrix (GLCM) features and labels (can embed or not). As in the proposed model, the optimal frame prediction is carried out using deep belief network (DBN) framework; the obtained data are then trained in the model. The optimal frames could be predicted in an efficient manner while testing. The significant contribution of this work concerns the optimization of hidden neurons in the DBN framework, which helps to enhance prediction accuracy. At last, the “watermark embedding process” and “watermark extraction process” are done by which the image could be embedded within the optimal frames.

The protection of medical images against unauthorized access and tampering is paramount. This paper presents a robust watermarking framework that integrates Discrete Wavelet Transform (DWT), Hessenberg Decomposition (HMD), Singular Value Decomposition (SVD), and Arnold Scrambling to enhance the security of medical images. By applying DWT to decompose the medical image into frequency subbands and embedding the watermark into the most significant subband, the proposed algorithm ensures minimal impact on image quality. HMD simplifies the subband matrix, while SVD extracts and manipulates the essential features of the image. Arnold Scrambling is employed to further secure the watermark image before embedding. Experimental results on various medical image datasets demonstrate the algorithm’s effectiveness in maintaining imperceptibility, with a peak signal-to-noise ratio (PSNR) of up to 49 dB, and robustness against common image processing attacks, such as compression and noise addition. The proposed scheme achieves a balance between imperceptibility and robustness, making it suitable for securing medical images in digital environments. The proposed scheme has been implemented on different medical datasets and the performance is evaluated in terms of its imperceptibility and robustness. The PSNR value achieved by the proposed work is 49 dB which proves that the embedded watermark image is imperceptible while the NC value achieved is higher than 0.9 against most of the attacks, hence proves its robustness against multiple attacks.

In this paper, Digital Watermarking is carried out in the frequency domain and the technique proposed uses single level Stationary Wavelet Transform (SWT) along with Singular Value Decomposition (SVD). SWT is used over other transformations because of its non-decimation and shift invariance property. The singular values of SWT transformed watermark image is embedded into the singularly decomposed HH sub-band (sub-image) of R, G or B channel of a Host color image. The experimental results of watermarked images shows increase in Peak Signal to Noise Ratio (PSNR) and the extracted watermark image is highly correlated with the original watermark for various types attacks.

The success of internet technology transform the world of technology and fashioned our life such a lot easier. The matter of duplication and unauthorized use of information become a great threat within the field of technology. To beat these problems, techniques like digital watermarking, steganography and cryptography were introduced. The approach of embedding a secret data associated with the digital signal inside the signal itself is digital watermarking. For embedding and detecting the watermark different techniques are used; spatial domain techniques like Least Significant Bit (LSB) and Patch Work Algorithm, then the frequency domain techniques such as Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Discrete Fourier Transform (DFT) are some of them. Combination of DCT & DWT technique for digital watermarking is proposed here. The proposed methodology is implemented in MATLAB 2017a simulator and result is analysed using evaluation parameters like Peak Signal to Noise Ratio (PSNR) & Mean Square Error (MSE). PSNR value obtained for the case of watermarking using DWT is 27.46 and MSE value is 17.45. The value of PSNR for watermarking using DCT is 41.4 and for MSE the value is 0.67. The PSNR and MSE values obtained for watermarking using LSB technique is 50.55 and 0.58. The proposed method improves the watermarked image quality and the MSE & PSNR value obtained is 0.52 and 51.017 respectively.

We propose a learned method for stereo image compression that leverages the similarity of the left and right images in a stereo pair due to overlapping fields of view. The left image is compressed by a learned compression method based on an autoencoder with a hyperprior entropy model. The right image uses this information from the previously encoded left image in both the encoding and decoding stages. In particular, for the right image, we encode only the residual of its latent representation to the optimally shifted latent of the left image. On top of that, we also employ a stereo attention module to connect left and right images during decoding. The performance of the proposed method is evaluated on two benchmark stereo image datasets (Cityscapes and InStereo2K) and outperforms previous stereo image compression methods while being significantly smaller in model size.

Digital watermarking provides the solution for problems such as copyright and authentication in today’s digital world of communication. Trustworthy watermarking techniques should provide the protection for rightful ownership, robustness and resist against attacks such as filtering and modification. In this paper, we proposed an efficient watermarking scheme based on singular value decomposition (SVD) and quantum Hilbert image scrambling. In the proposed watermarking scheme, first watermark is scrambled using quantum Hilbert image scrambling then perform alpha blending on scrambled watermark and SVD transformed cover image followed by alpha mixing operation. Finally, perform SVD operation of the generated signal; after alpha blending operation, we get watermarked image. The experimental results show strength of the proposed technique in terms of the robustness and security. Performance of the proposed scheme is tested on different image quality parameters of watermarked and host image. These are peak signal-to-noise ratio (PSNR), mean square error (MSE) and normalized cross-correlation (NCC), average difference (AD), maximum difference (MD), and structural contents (SC).KeywordsSingular value decompositionWatermarkQuantum Hilbert image scramblingSteganography

In a watermarking system, robustness evaluation should be made if invisibility criteria are satisfied. For this purpose, we propose to measure the perceptual impact of a watermark on the image using the well known peak signal-to-noise ratio (PSNR) and additionally a spatial masking model that indicates "bad" pixels between the original and watermarked images. Two perceptual watermarks using pixel-wise masking in the wavelet domain are being tested, limiting the watermark strength such that the PSNR is 35 dB and in average the percentage of affected pixels is less than 25%. Extensive tests were made on several watermarked images and for different types of attacks (JPEG compression, median filtering, resizing, cropping and gamma correction) in order to see the effect of these attacks on the detector results. Simulations show some limitations of these techniques under the invisibility constraint.

In this paper, an effective algorithm for providing copyright protection is proposed by using a new embedding strategy for Discrete Wavelet Transform-based video watermarking. Discrete Wavelet Transform (DWT) is applied on the video, to convert the spatial data into frequency domain, having low pass and high pass components. The low frequency component is used for generating the key, by using the watermark image and the binarized Low frequency part (LL) of the video frame. Same procedure is applied on each frame to generate the key for corresponding frame. This generated key should be used at receiver for extracting the watermark which provides copyright protection. Blind watermarking technique is used in this paper which require only key to extract the embedded watermark. The original video is not required during extraction. To criticize the robustness of algorithm, the original watermark image is compared with extracted watermark image after several attacks and their Peak Signal to Noise Ratio (PSNR), Normalized Correlation Coefficient (NC) and Structural Similarity index (SSIM) are calculated. The experimental results demonstrate that the watermark is invisible and it is robust against the various attacks and addition of noise to the video.

<p>Protection of Medical image contents becomes the important issue in computer network security. Digital Watermarking has becomes a promising technique for medical content authentication, it allows to embed relevant information with the image, which provides confidentiality, integrity and authentication by embedding Digital Signature (DS) with the Medical image. In this paper we focus on need for reversible watermarking, Medical Image Compression and security related problems in medical images, it comparing the performances of various lossless watermarking techniques for various medical image modalities like MRI (Magnetic Resonance Imaging), US (Ultrasonic), CT (Computed Tomography), Endoscopic and Angiographic images. Region of Interest (ROI) supporting lossless watermarking systems only considered for discussions. Performance of all lossless watermarking with Digital Signature is analyzed by means of four parameters Capacity Rate, PSNR (Peak Signal to Noise ratio), NPCR (Number of Pixel Change Rate) and Compression Ratio (CR). This Paper also introduces new mechanism for open network security for medical images. This lossless watermarking is responsible for recovering the altered medical image content of the system.</p>

A digital watermark is an invisible mark embedded in a digital image which may be used for copyright protection. This paper proposes that Fourier-Mellin transform-based invariants can be used for digital image watermarking. The embedded marks may be designed to be unaffected by any combination of rotation, scale and translation transformations. The original image is not required for extracting the embedded mark.

In this paper, we propose HESIC, an end-to-end trainable deep network for stereo image compression (SIC). To fully explore the mutual information across two stereo images, we use a deep regression model to estimate the homography matrix, i.e., H matrix. Then, the left image is spatially transformed by the H matrix, and only the residual information between the left and right images is encoded to save bitrates. A two-branch auto-encoder architecture is adopted in HESIC, corresponding to the left and right images, respectively. For entropy coding, we use two conditional stereo entropy models, i.e., Gaussian mixture model (GMM) based and context based entropy models, to fully explore the correlation between the two images to reduce the coding bit-rates. In decoding, a cross quality enhancement module is proposed to enhance the image quality based on inverse H matrix. Experimental results show that our HESIC outperforms state-of-the-art SIC methods on InStereo2K and KITTI datasets both quantitatively and qualitatively. Code is available at https://github.com/ywz978020607/HESIC.

In recent years, with the rapid development of internet, digital data has been widely used in various science and engineering applications. But, this development has also led to several threats and attacks from the intruders. Most of the organizations are very much concerned about the Illegal copyright activities which have become a series issue. A number of copyright protection schemes have been developed in the literature. Watermarking is one of the best known methods for copyright protection. Earlier Bit Error Rate (BER) is been observed to provide good results for choosing the optimal feature region from the watermarked image. However it fails to produce more accurate image quality measures. This research work mainly focuses on a novel technique implemented based on the simulated attacking approach and the GA-based Multi Dimensional Knapsack Problem (MDKP) solving technique to select the most sufficient feature regions for robust digital image watermarking without affecting the image quality. A major limitation in existing methods is the security problems which lead to decrease in the integrity of digital watermarking scheme. With that problem, the definite optimization problems (variant problems) cannot be resolved using genetic algorithms. To focus on the above mentioned problems, the proposed work uses two approaches. Chaotic Iterative Process encryption scheme is used to solve the security problems for watermarking image. Next, the optimal feature points can be chosen from the watermarked image by Modified version of Peak Signal Noise Ratio (MPSNR). MPSNR is based on the enhanced quality measurement of which point opposes the predefined and undefined attacks. With those two approaches the PSO algorithm is employed as a search strategy for identifying an optimal feature subset. The experimental results illustrates that comparing with various well-known feature-based techniques, the proposed method shows better performance in robust and more secure digital watermarking.

Digital watermarking is a growing paradigm in information science and communication systems that can be applied in the medical field. In general, a digital watermark is a mechanism for protecting digital information's copyright. Various algorithms have been developed till date to implement watermarking in order to safeguard data. The Discrete Cosine Transform is one such well-known algorithm (DCT). This algorithm is a frequency domain technique that provides security against attacks. ECG signals can also be compressed using DCT in electrocardiography. The proposed method primarily focuses on the application of digital watermarking techniques on patient medical records. The solution's goal is to preserve patients' medical records by integrating them in their MRI scan reports. As a result, the attacker will only be able to see the MRI scan report, while the health information will be buried. Singular value decomposition is used to compress the MRI scan report in this case (SVD). The medical health record, on the other hand, is encrypted using Advanced Encryption Standard (AES) algorithm, and the cipher picture obtained is employed in the embedding procedure. The DCT technique is used to embed the compressed MRI scan (cover image) and cipher image (watermark image). The embedded medical health record can be extracted by reverse application of DCT. The extracted cipher image is then decrypted using AES decryption algorithm. The produced watermark image’s (health record) quality is then assessed using a set of standards referred to as the Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE).