Image Watermarking Technique Research Articles

Social Media Authentication and Combating Deepfakes using Semi-fragile Invisible Image Watermarking

With the significant advances in deep generative models for image and video synthesis, Deepfakes and manipulated media have raised severe societal concerns. Conventional machine learning classifiers for deepfake detection often fail to cope with evolving deepfake generation technology and are susceptible to adversarial attacks. Alternatively, invisible image watermarking is being researched as a proactive defense technique that allows media authentication by verifying an invisible secret message embedded in the image pixels. A handful of invisible image watermarking techniques introduced for media authentication have proven vulnerable to basic image processing operations and watermark removal attacks. In response, we have proposed a semi-fragile image watermarking technique that embeds an invisible secret message into real images for media authentication. Our proposed watermarking framework is designed to be fragile to facial manipulations or tampering while being robust to benign image-processing operations and watermark removal attacks. This is facilitated through a unique architecture of our proposed technique consisting of critic and adversarial networks that enforce high image quality and resiliency to watermark removal efforts, respectively, along with the backbone encoder-decoder and the discriminator networks. This allows images shared over the Internet to retain the verifiable watermark as long as facial manipulations or any other Deepfake modification technique is not applied. Thorough experimental investigations on SOTA facial Deepfake datasets demonstrate that our proposed model can embed a \(64\) -bit secret as an imperceptible image watermark that can be recovered with a high-bit recovery accuracy when benign image processing operations are applied while being non-recoverable when unseen Deepfake manipulations are applied. In addition, our proposed watermarking technique demonstrates high resilience to several white-box and black-box watermark removal attacks. Thus, obtaining state-of-the-art performance.

Robust image tamper detection and recovery with self-embedding watermarking using SPIHT and LDPC

In today’s digital landscape, the pervasive use of digital images across diverse domains has led to growing concerns regarding their authenticity and reliability. The potential for malicious manipulation of these images underscores the critical need to develop robust methods for detecting tampering and ensuring their integrity. Fragile watermarking has been found to have extensive applications for tamper detection and recovery. An image watermarking technique for tamper detection, correction, and recovery is presented in this study. The proposed method employs a self-embedding method to generate the reference watermark from the original image, which has the advantage of superior tamper detection, localization, recovery capabilities, and robustness against attacks. Set Partitioning in the Hierarchical Tree (SPIHT) algorithm is applied to generate a reference watermark of the original image. Low-Density Parity Check (LDPC) is employed for error correction, providing higher-quality reconstruction to recover the original image. Schur decomposition processed the watermarked image blocks to generate authentication bits for each block to enhance tampering detection. The proposed watermarking method was evaluated using PSNR, SSIM, BER, and NC metrics for grayscale and colored images. The technique demonstrated high robustness in tamper detection and recovery against various malicious attacks. Comparative analysis with existing methods shows the efficacy of the proposed method.

Enhancing image watermarking efficiency through advanced deep learning: A novel approach with modified Res-Ception blocks and practical applications in modern scenarios

In recent years, digital media has become a prevalent form of information exchange, necessitating robust measures for privacy, data safety, and copyright protection. Image watermarking is one of the tools that can be employed for these tasks. Research on blind image watermarking techniques is lacking, and there is a gap between theoretical findings and practical implementations to address contemporary issues such as photo metadata privacy, AI generated image recognition, etc. The proposed research introduces a revolutionary deep learning-based image watermarking approach in order to address these issues. This scheme is designed to overcome the contemporary challenges related to image watermarking by being blind, efficient and robust. It also has an improved PSNR and beats state-of-the-art in various attacks. The model outperforms existing deep learning frameworks in terms of fidelity. All these are achieved by creating a modified and much more efficient form of Inception Resnet block, coined ResCeption Block, for use in effective learning of watermarking networks. The research also presents an application of watermarking in the identification of AI generated images and proposes a novel and robust algorithm for metadata encryption of digital images and enforce digital media security. Rigorous testing has been performed on benchmark datasets, and comparisons have been done against state-of-the-art and a PSNR of 43.12 dB has been achieved.

New quantum color image watermarking technique (NQCIWT)

The crazy, unconscious use of the Internet, and the increase in cybercrime and hacking, which resulted in the loss of a large number of sensitive data, the risk of piracy, etc. were the motivation for protecting rights, securing and hiding essential information such as images, audio, etc. Watermarking was a popular security, copyright and privacy approach and one of the problems in modern quantum digital computing that produced more powerful quantum protection techniques than the classical one that used classical techniques such as digital wavelet transformation and Hungarian algorithms. Recent advances in quantum image representation and processing help to manipulate data hidden. This paper proposes a new watermark technique in which a color watermark image is hidden within another color-carrying image after transformation into the quantum domain using the recently completed quantum color image representation technique. Finally, a comparison is made between the proposed method and the most recent watermarking techniques is establish in terms of PSNR (peak signal-to-noise ratio), which shows the high precision of the proposed technique and improves the imperceptibility compared to the related schemes.

Statistical learning based blind image watermarking approach

Image watermarking technology poses a significant challenge, requiring a delicate balance between robustness, imperceptibility, and capacity. To solve the balancing problem, a statistical learning based blind image watermarking technique is proposed in this paper. The idea of the proposed method to solve the balancing problem is to enhance imperceptibility and robustness while accommodating sufficient watermarks. Firstly, the local low-order pseudo-Zernike moments (PZM) magnitude in the discrete non-separable Shearlet transform (DNST) domain is constructed as the embedding domain. To ensure stability in embedding positions, we propose an edge embedding strategy. Secondly, by analyzing the statistical characteristics and multi-correlations of DNST-PZM magnitudes, a multi-correlation vector model based on the skew student's-t mixture (SSM) and hidden Markov tree (HMT) is designed. Finally, leveraging the vector SSM-HMT model and the maximum likelihood (ML) criterion, we derive the closed-form decoder expression. Comparative analysis with state-of-the-art methods demonstrates the superior imperceptibility and robustness of our proposed approach in accommodating the same capacity watermark.

A robust digital image watermarking technique in LWT-DCT domain using particle swarm optimization and statistical distortion correction

A robust digital image watermarking technique in LWT-DCT domain using particle swarm optimization and statistical distortion correction

Deep Learning-Based Watermarking Techniques Challenges: A Review of Current and Future Trends

The digital revolution places great emphasis on digital media watermarking due to the increased vulnerability of multimedia content to unauthorized alterations. Recently, in the digital boom in the technology of hiding data, research has been tending to perform watermarking with numerous architectures of deep learning, which has explored a variety of problems since its inception. Several watermarking approaches based on deep learning have been proposed, and they have proven their efficiency compared to traditional methods. This paper summarizes recent developments in conventional and deep learning image and video watermarking techniques. It shows that although there are many conventional techniques focused on video watermarking, there are yet to be any deep learning models focusing on this area; however, for image watermarking, different deep learning-based techniques where efficiency in invisibility and robustness depends on the used network architecture are observed. This study has been concluded by discussing possible research directions in deep learning-based video watermarking.

A novel dynamic image watermarking technique with features inspired by quantum computing principles

This research proposes a novel dynamic image watermarking technique with features inspired by quantum computing principles. This method encodes binary values into qubits and embeds a watermark into an original image. The watermarking process is achieved by utilizing quantum circuits to manipulate the qubits representing the pixel values of the original and watermark images. To extract the watermark, encode each pixel value into a qubit, combine them using quantum operations, and then measure the resultant quantum state. This technique ensures the integrity and authenticity of the image by embedding a watermark that can be extracted with high fidelity. Simulation results show that our technique successfully embeds watermarks while maintaining picture quality. Moreover, this method exhibits robustness against common image processing attacks, highlighting its potential for secure image verification applications.

Optimized and secure digital image watermarking technique using Henon mapping in redundant domain

Optimized and secure digital image watermarking technique using Henon mapping in redundant domain

A review of digital watermarking techniques: Current trends, challenges and opportunities

The rapid progress in the transfer of information and its availability are the reasons behind the widespread use of the Internet. Document images are the most complicated and challenging category among various forms of digital data to which watermarking can be applied for its security and authentication. Watermarking of the document image is a very difficult task, since these images have very limited redundancy. Because of this factor there has been very little research in this area. A comprehensive research should be carried out to ensure the effective assessment, review as well as implementation of document image watermarking techniques. This paper reviews the existing document image watermarking by considering different evaluation parameters. Based on this review, a variety of challenges and directions have been prescribed for the development of effective watermarking techniques for document images.

FaceSigns: Semi-Fragile Watermarks for Media Authentication

Manipulated media is becoming a prominent threat due to the recent advances in realistic image and video synthesis techniques. There have been several attempts at detecting synthetically tampered media using machine learning classifiers. However, such classifiers do not generalize well to black-box image synthesis techniques and have been shown to be vulnerable to adversarial examples. To address these challenges, we introduce FaceSigns — a deep learning based semi-fragile watermarking technique that allows media authentication by verifying an invisible secret message embedded in the image pixels. Instead of identifying and detecting manipulated media using visual artifacts, we propose to proactively embed a semi-fragile watermark into a real image or video so that we can prove its authenticity when needed. FaceSigns is designed to be fragile to malicious manipulations or tampering while being robust to benign operations such as image/video compression, scaling, saturation, contrast adjustments etc. This allows images and videos shared over the internet to retain the verifiable watermark as long as a malicious modification technique is not applied. We demonstrate that our framework can embed a 128 bit secret as an imperceptible image watermark that can be recovered with a high bit recovery accuracy at several compression levels, while being non-recoverable when unseen malicious manipulations are applied. For a set of unseen benign and malicious manipulations studied in our work, our framework can reliably detect manipulated content with an AUC score of 0.996 which is significantly higher than prior image watermarking and steganography techniques.

Image Watermarking Using Discrete Wavelet Transform and Singular Value Decomposition for Enhanced Imperceptibility and Robustness

Digital multimedia elements such as text, image, audio, and video can be easily manipulated because of the rapid rise of multimedia technology, making data protection a prime concern. Hence, copyright protection, content authentication, and integrity verification are today’s new challenging issues. To address these issues, digital image watermarking techniques have been proposed by several researchers. Image watermarking can be conducted through several transformations, such as discrete wavelet transform (DWT), singular value decomposition (SVD), orthogonal matrix Q and upper triangular matrix R (QR) decomposition, and non-subsampled contourlet transform (NSCT). However, a single transformation cannot simultaneously satisfy all the design requirements of image watermarking, which makes a platform to design a hybrid invisible image watermarking technique in this work. The proposed work combines four-level (4L) DWT and two-level (2L) SVD. The Arnold map initially encrypts the watermark image, and 2L SVD is applied to it to extract the s components of the watermark image. A 4L DWT is applied to the host image to extract the LL sub-band, and then 2L SVD is applied to extract s components that are embedded into the host image to generate the watermarked image. The dynamic-sized watermark maintains a balanced visual impact and non-blind watermarking preserves the quality and integrity of the host image. We have evaluated the performance after applying several intentional and unintentional attacks and found high imperceptibility and improved robustness with enhanced security to the system than existing state-of-the-art methods.

Robust Image Watermarking using DWT, DCT, and PSO with CNN-Based Attack Evaluation

Digital Content Protection is one of the most significant research areas that lies at the intersection of cyber-security and multimedia processing. Protect multimedia content from copyright violation, unauthorized use, replication, and online content theft is needed. Digital Image Watermarking is utilized to preserve the copyright of different digital images from forgery. Various techniques had been developed in this regard with two main issues, the method robustness and the resistance against various types of attacks like, Salt and Pepper noise, filtering and blurring. Current Digital watermarking techniques may reduce the quality of the original digital media content if it is not robust. The purpose of this research is to create a robust image watermarking technique against different attack types such as salt and pepper noise and Gaussian noise, ensuring the image content is protected. Specifically, this study proposed a new mechanism for Image watermarking based on combining Discrete Wavelet Transform (DWT), and Discrete Cosine Transform (DCT). Additionally, Particle Swarm Optimization (PSO) was applied to perform the optimization for both the embedding and extraction processes. At the final stage we assess the proposed approach against some types of attacks such as Additive White Gaussian Noise (AWGN). The Denoising Convolutional Neural Network, (DnCNN) was used to evaluate the mechanism against AWGN. For testing, we utilized measures such as Peak Signal-to-Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC). The experimental results of implementing our proposed method for embedding and extracting watermarks across various host image sizes were encouraging, achieving a PSNR ratio of 0.998 and an NCC of 1 in the absence of attacks. Additionally, our evaluation revealed that a specific type of denoising attack, while damaging the watermark (though not completely), actually improved the image quality. It is also important to highlight that our findings surpassed those reported in existing literature, with the PSNR and NCC values serving as evidence of this superior performance.

A charge system search based watermarking technique for gray-scale images using DWT–SVD

A charge system search based watermarking technique for gray-scale images using DWT–SVD

A Robust and Imperceptible Dark Frame Selection Using Advanced Squeezed Convoluted Ebola Neural Network-Based Video Watermarking Scheme with All Phase Discrete Cosine Biorthogonal Transform

Watermarking has been developed as a beneficial approach to resolve security problems such as copyright defense, legitimate ownership, and authenticity of digital data. Grayscale or binary images and color images are the most important types of embedded watermarks in the existing video watermarking schemes, while dark frame watermarking is barely used. A new robust blind video watermarking system with an Advanced Squeezed Convoluted Ebola Neural Network (ASCENN) approach is suggested to secure copyright, reduce noise, and authenticate the authenticity of dark frames. The proposed method selects the dark frames from the extracted input video. Then the selected dark frames are embedded and extracted using Finite Ridgelet Transform (FRT), All Phase Discrete Cosine Biorthogonal Transform (APDCBT), and Singular Value Decomposition (SVD) techniques. The robustness of the introduced approach is established by evaluating it through various image watermarking techniques and evaluating its performance against geometric, nongeometric, and combinational attacks. While comparing with the other existing video watermarking approaches, the proposed method has obtained a maximum correlation coefficient of 1, and the signal-to-noise ratio of 75[Formula: see text]dB, at minimum error rate of 0.0215, respectively.

Selection of Digital Watermarking Techniques for Medical Image Security by Using the Fuzzy Analytical Hierarchy Process

Background: The watermarking technique is a security algorithm for medical images and the patient's information. Watermarking is used for maintaining the robustness, integrity, confidentiality, authentication, and complexity of medical images. Objective: The selection of medical image watermarking technique is multi-criteria decision-making and an automatic way of algorithm selection for security and privacy. However, it is difficult to select a better watermarking technique through traditional selection techniques. Methods: To deal with this problem, a multicriteria-based fuzzy analytic hierarchy process (FAHP) was proposed. This method is applied for algorithm selection for the security of medical images in healthcare. In this method, we first determined the list of criteria and alternatives, which directly affect the decision of the medical image security algorithm. Then, the proposed method was applied to the criteria and alternatives. Results: We provided the rank according to the obtained weights of the algorithm. Furthermore, the overall results and ranking of the algorithms were also presented in this article. Conclusion: Integrity was found to have the highest weight (0.509) compared to the other criteria. The weight of the other criteria, namely authentication, was 0.165, robustness was 0.151, confidentiality was 0.135, and complexity was 0.038. Thus, in terms of ranking, integrity was reported to be of the highest priority among the five criteria attributes.

An effective embedding algorithm for blind image watermarking technique based on Hessenberg decomposition

An effective embedding algorithm for blind image watermarking technique based on Hessenberg decomposition

A new efficient anaglyph 3D image and video watermarking technique minimizing generation deficiencies

A new efficient anaglyph 3D image and video watermarking technique minimizing generation deficiencies

Review of Image Forensic Techniques Based on Deep Learning

Digital images have become an important carrier for people to access information in the information age. However, with the development of this technology, digital images have become vulnerable to illegal access and tampering, to the extent that they pose a serious threat to personal privacy, social order, and national security. Therefore, image forensic techniques have become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely applied in the field of image forensics and the performance achieved has significantly exceeded the conventional forensic algorithms. This survey compares the state-of-the-art image forensic techniques based on deep learning in recent years. The image forensic techniques are divided into passive and active forensics. In passive forensics, forgery detection techniques are reviewed, and the basic framework, evaluation metrics, and commonly used datasets for forgery detection are presented. The performance, advantages, and disadvantages of existing methods are also compared and analyzed according to the different types of detection. In active forensics, robust image watermarking techniques are overviewed, and the evaluation metrics and basic framework of robust watermarking techniques are presented. The technical characteristics and performance of existing methods are analyzed based on the different types of attacks on images. Finally, future research directions and conclusions are presented to provide useful suggestions for people in image forensics and related research fields.

Image watermarking techniques based on Schur decomposition and various image invariant moments: a review

Image watermarking techniques based on Schur decomposition and various image invariant moments: a review