Robust Watermarking Research Articles

A Decentralized Digital Watermarking Framework for Secure and Auditable Video Data in Smart Vehicular Networks

Thanks to the rapid advancements in Connected and Automated Vehicles (CAVs) and vehicular communication technologies, the concept of the Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) and big data promotes the vision of an Intelligent Transportation System (ITS). An ITS is critical in enhancing road safety, traffic efficiency, and the overall driving experience by enabling a comprehensive data exchange platform. However, the open and dynamic nature of IoV networks brings significant performance and security challenges to IoV data acquisition, storage, and usage. To comprehensively tackle these challenges, this paper proposes a Decentralized Digital Watermarking framework for smart Vehicular networks (D2WaVe). D2WaVe consists of two core components: FIAE-GAN, a novel feature-integrated and attention-enhanced robust image watermarking model based on a Generative Adversarial Network (GAN), and BloVA, a Blockchain-based Video frames Authentication scheme. By leveraging an encoder–noise–decoder framework, trained FIAE-GAN watermarking models can achieve the invisibility and robustness of watermarks that can be embedded in video frames to verify the authenticity of video data. BloVA ensures the integrity and auditability of IoV data in the storing and sharing stages. Experimental results based on a proof-of-concept prototype implementation validate the feasibility and effectiveness of our D2WaVe scheme for securing and auditing video data exchange in smart vehicular networks.

An adaptive robust watermarking scheme based on chaotic mapping

Digital images have become an important way of transmitting information, and the risk of attacks during transmission is increasing. Image watermarking is an important technical means of protecting image information security and plays an important role in the field of information security. In the field of image watermarking technology, achieving a balance between imperceptibility, robustness, and embedding capacity is a key issue. To address this issue, this paper proposes a high-capacity color image adaptive watermarking scheme based on discrete wavelet transform (DWT), Heisenberg decomposition (HD), and singular value decomposition (SVD). In order to enhance the security of the watermark, Logistic chaotic mapping was used to encrypt the watermark image. By adaptively calculating the embedding factor through the entropy of the cover image, and then combining it with Alpha blending technology, the watermark image is embedded into the Y component of the YCbCr color space to enhance the imperceptibility of the algorithm. In addition, the robustness of the algorithm was further improved through singular value correction methods. The experimental results show that the average PSNR and SSIM of the watermarking scheme are 45.3437dB and 0.9987, respectively. When facing various attacks, the average NCC of the extracted watermark reaches above 0.95, indicating good robustness. The embedding capacity of this scheme is 0.6667bpp, which is higher than other watermarking schemes, and the average running time is 1.1136 seconds, which is better than most schemes.

A Compressed Domain Robust Video Watermarking Resists Geometric Attack

The widespread utilization of mobile devices and the proliferation of video content, facilitated by communication networks, has unfortunately led to issues such as data piracy, illegal usage, and unauthorized sharing. To address these challenges, digital watermarking emerges as a crucial tool, serving purposes such as authentication, maintaining integrity, and identifying rightful owners. In the realm of video watermarking, two primary categories exist: watermarking in the compressed domain and watermarking in the uncompressed domain. Compressed domain watermarking involves expedited data processing through partial video decoding, whereas uncompressed domain watermarking requires more time. This article proposes a novel video watermarking methodology designed specifically to withstand geometric attacks. The proposed method aims to dynamically embed watermarks within video content, thereby enhancing resilience against various geometric distortions. Geometric attacks, including scaling, rotation, cropping, and translation, pose significant challenges in digital watermarking and are thus a focal point of this research. The objective is to integrate this approach with complex video coding standards, leveraging compression domain techniques to fortify resistance against geometric attacks. Tests show that the suggested video watermarking system works well. Not only does it excel in safeguarding against geometric attacks, but it also mitigates perceptual quality degradation, thus ensuring a balance between robustness and visual fidelity. As the prevalence of video content continues to grow, robust watermarking techniques like the one presented here play a vital role in preserving intellectual property rights and ensuring the integrity of multimedia content in the digital age.

Adaptive Robust Watermarking for Resisting Multiple Distortions in Real Scenes

Adaptive Robust Watermarking for Resisting Multiple Distortions in Real Scenes

Novel optimal region based robust watermarking using modified dragonfly optimization algorithm

Novel optimal region based robust watermarking using modified dragonfly optimization algorithm

Adaptive robust watermarking scheme based on RIME optimization with integer wavelet transform, singular value decomposition, and Hadamard transform

Adaptive robust watermarking scheme based on RIME optimization with integer wavelet transform, singular value decomposition, and Hadamard transform

A Transformer-based invertible neural network for robust image watermarking

For the existing encoder-noise-decoder (END) based watermarking models, since the coupling between the encoder and the decoder is weak, the encoder generally embeds certain redundant features into the cover image to enable the decoder to extract watermark completely, which will affect watermarking invisibility. To address this problem, this paper proposes a Transformer-based invertible neural network (INN) for robust image watermarking (IWFormer). In order to effectively reduce redundant features, the INN framework is utilized for the watermark embedding and extracting processes, so that the encoded features are highly consistent with the features required for decoding. For enhancing watermarking robustness, an affine Transformer module is designed by mining the global correlation of the cover image. In addition, considering that the human visual system is sensitive to low-frequency variations, the wavelet low-frequency sub-band loss is deployed to guide watermark to be embedded in middle- and high-frequency components, thus further increasing the quality of the encoded images. Experimental results demonstrate that compared with the existing state-of-the-art watermarking models, the proposed IWFormer owns remarkable advantages in terms of both watermarking invisibility and robustness.

Image sterilization via overwriting

Sterilization refers to the method of removing hidden information from images, effectively eradicating malicious data embedded within digital media. Existing methods are hampered by dual limitations: insufficient resilience to cutting-edge information hidden methods, coupled with the challenge of image restoration in quality. In response to these challenges, this paper proposes a novel method composed of overwriting attack and diffusion optimization stages. Employing the concept of pixel destruction, the overwriting method conducts a more thorough attack on hidden information while preserving sufficient information for image reconstruction and further diffusion optimization. This strategy is not bound to any particular information hiding method, making it effective against a wide range of steganography and watermarking techniques by systematically disrupting every pixel in the image. Diffusion optimization makes full use of the generative capabilities of diffusion models trained on large datasets, enabling higher quality image enhancements for a variety of scene images.Our method stands out in comparison to state-of-the-art studies, as experimental outcomes demonstrate its capability to maintain superior image integrity while efficiently eliminating hidden information across various steganography and watermarking techniques. Notably, when confronted with deep robust watermarking schemes, our approach exhibits a significant improvement over preceding methods. For instance, the removal rate improves significantly from 39.42% to 87.78% with higher quality restoration when compared to SC-Net. Our method delivers superior outcomes in the task of image sterilization, while presents a new challenge to the robustness of image information hiding methods and contributes to network security.

A robust watermarking approach for medical image authentication using dual image and quorum function

To safeguard the identity and copyright of a patient’s medical documents, watermarking strategies are widely used. This work provides a new dual image-based watermarking approach using the quorum function (QF) and AD interpolation technique. AD interpolation is used to create the dual images which helps to increase the embedding capacity. Moreover, the rules for using the QF are designed in such a way, that the original bits are least affected after embedding. As a result, it increases the visual quality of the stego images. A shared secret key has been employed to protect the information hidden in the medical image and to maintain the privacy and confidentiality. The experimental result using PSNR, SSIM, NCC, and EC shows that the suggested technique gives an average PSNR of 68.44 dB and SSIM is close to 0.99 after inserting 786432 watermark bits, which demonstrates the superiority of the scheme over other state-of-the-art schemes.

Quaternion attention-based JND model for macrophotography image watermarking

With the advancement of imaging technology, macrophotography images (MPIs) have become a popular research topic. Unlike natural images, MPIs often feature sharp foregrounds and blurred backgrounds, leading to distinct perceptual characteristics in estimation. As the number of MPIs grows rapidly, concerns over image quality and security increase. Robust watermarking techniques have been introduced to address these challenges. Just Noticeable Difference (JND) has been widely used in quantization-based watermarking frameworks. However, existing JND models handle each image area with a single-level perceptual attention. Visual attention in Quaternion Discrete Wavelet Transform (QDWT), which can reflect the Multi-level perceptual attention feature. Therefore, we propose a new method called Quaternion Attention-based Just Noticeable Difference model for MPIs Watemarking (QAJnd-MW) for watermarking MPIs. This method uses visual attention mechanisms, recognizing that the HVS is more sensitive to attention regions. We generate a masking effect in the JND field. The input image undergoes QDWT to explore multi-scale features. The multi-scale feature maps, with multi-directional luminance and multi-channel color, help create local and global attention maps, which are fused to form the final attention map. Specifically, considering both attention-based masking effects, the quaternion attention-guided JND model is designed for a robust MPI watermarking framework, aiming to further improve MPI security. Extensive experiments on the MP2020 and Blur Detection datasets show that the proposed model significantly improves robustness against JPEG compression attacks, reducing the bit error rate (BER) by up to 12%. Additionally, the model performs well against other attacks, such as those in online social networks, with lower BER than current state-of-the-art techniques.

A Video Dual-Domain Blind Watermarking Algorithm Based on Hadamard Transform

Addressing the compatibility challenges surrounding the robustness and reversibility of existing video watermarking techniques, this study introduces a novel video dual-domain blind watermarking algorithm leveraging the Hadamard transform. Specifically tailored for H.264 video copyright protection, the algorithm initially organizes video frames and identifies key frames for watermark embedding. Prior to embedding, the robust watermark undergoes coding preprocessing to optimize its integration. Subsequently, a 4×4 block is expanded based on the selected embedding position within the frame, followed by the application of the Hadamard transform to the enlarged block. The 1-bit robust watermark information is then embedded via the coefficient pair located in the first row of the Hadamard coefficient matrix corresponding to the expanded block. Additionally, a reversible watermark, designed to mitigate the distortions introduced during robust embedding, is generated and embedded into the remaining coefficients of the coefficient matrix using reversible embedding techniques. During watermark extraction, the dual-domain watermark can be retrieved exclusively through reversible extraction methodologies by analyzing the size relationship of coefficient pairs, eliminating the need for access to the original video data. To bolster the algorithm’s robustness, a majority-subordinate voting system is devised and implemented, effectively enhancing its resilience. Experimental findings demonstrate that, compared to similar approaches, this algorithm not only enhances the reversibility of video restoration but also exhibits superior robustness and meets the requirements for imperceptibility.

Copy-Right protection of color videos using robust watermarking based geometrically invariant moments of fractional orders and logistic sine cosine chaotic map

Copy-Right protection of color videos using robust watermarking based geometrically invariant moments of fractional orders and logistic sine cosine chaotic map

Robust Reversible Watermarking Scheme in Video Compression Domain Based on Multi-Layer Embedding

Most of the existing research on video watermarking schemes focus on improving the robustness of watermarking. However, in application scenarios such as judicial forensics and telemedicine, the distortion caused by watermark embedding on the original video is unacceptable. To solve this problem, this paper proposes a robust reversible watermarking (RRW)scheme based on multi-layer embedding in the video compression domain. Firstly, the watermarking data are divided into several sub-secrets by using Shamir’s (t, n)-threshold secret sharing. After that, the chroma sub-block with more complex texture information is filtered out in the I-frame of each group of pictures (GOP), and the sub-secret is embedded in that frame by modifying the discrete cosine transform (DCT) coefficients within the sub-block. Finally, the auxiliary information required to recover the coefficients is embedded into the motion vector of the P-frame of each GOP by a reversible steganography algorithm. In the absence of an attack, the receiver can recover the DCT coefficients by extracting the auxiliary information in the vectors, ultimately recovering the video correctly. The watermarking scheme demonstrates strong robustness even when it suffers from malicious attacks such as recompression attacks and requantization attacks. The experimental results demonstrate that the watermarking scheme proposed in this paper exhibits reversibility and high visual quality. Moreover, the scheme surpasses other comparable methods in the robustness test session.

Double-layer data-hiding mechanism for ECG signals

Due to the advancement in biomedical technologies, to diagnose problems in people, a number of psychological signals are extracted from patients. We should be able to ensure that psychological signals are not altered by adversaries and it should be possible to relate a patient to his/her corresponding psychological signal. As far as our awareness extends, none of the existing methods possess the capability to both identify and verify the authenticity of the ECG signals. Consequently, this paper introduces an innovative dual-layer data-embedding approach for electrocardiogram (ECG) signals, aiming to achieve both signal identification and authenticity verification. Since file name-based signal identification is vulnerable to modifications, we propose a robust watermarking method which will embed patient-related details such as patient identification number, into the medically less-significant portion of the ECG signals. The proposed robust watermarking algorithm adds data into ECG signals such that the patient information hidden in an ECG signal can resist the filtering attack (such as high-pass filtering) and noise addition. This is achieved via the use of error buffers in the embedding algorithm. Further, modification-sensitive fragile watermarks are added to ECG signals. By extracting and checking the fragile watermark bits, we can determine whether an ECG signal is modified or not. To ensure the security of the proposed mechanism, two secret keys are used. Our evaluation demonstrates the usefulness of the proposed system.

Background adaptive PosMarker based on online generation and detection for locating watermarked regions in photographs

Robust watermarking technology can embed invisible messages in screens to trace the source of unauthorized screen photographs. Locating the four vertices of the embedded region in the photograph is necessary, as existing watermarking methods require geometric correction of the embedded region before revealing the message. Existing localization methods suffer from a performance trade-off: either causing unaesthetic visual quality by embedding visible markers or achieving poor localization precision, leading to message extraction failure. To address this issue, we propose a background adaptive position marker, PosMarker, based on the gray level co-occurrence matrix and the noise visibility function. Besides, we propose an online generation scheme that employs a learnable generator to cooperate with the detector, allowing joint optimization between the two. This simultaneously improves both visual quality and detection precision. Extensive experiments demonstrate the superior localization precision of our PosMarker-based method compared to others.

A DNN robust video watermarking method in dual-tree complex wavelet transform domain

Deep learning is increasingly being applied in the field of robust watermarking. However, the existing deep learning-based video watermarking methods only uses spatial domain information as the input and the robustness against attacks such as H.264/AVC compression is still not strong. Therefore, this paper proposes a deep learning-based robust video watermarking method in dual-tree complex wavelet transform (DT-CWT) domain. The video frames are transformed into the DT-CWT domain and the suitable high-pass subbands are selected as candidate embedding positions. Then, the 2D and 3D convolutions are combined to extract both intra-frame spatial features and inter-frame temporal features for finding the stable and imperceptible coefficients for watermark embedding in the candidate positions. The convolutional attention module (CBAM) is used to further adjust the embedding coefficients and strengths. In addition, the attack layer, where a differentiable proxy is specially designed in this paper for the simulation of non-differentiable H.264/AVC compression, is introduced to generate distorted watermarked videos for improving the robustness against different attacks. Experimental results show that our method is superior to both the existing deep learning-based methods and traditional methods in the robustness against both spatial and temporal attacks while preserving high video quality. The source code is available at https://github.com/imagecbj/A-DNN-Robust-Video-Watermarking-Method-in-DT-CWT-Domain.

A robust medical image zero-watermarking algorithm using Collatz and Fresnelet Transforms

Zero-watermarking in medical images is an emerging field that focuses on calculating the invisible data (key) using medical imagery to ensure data integrity and authenticity without compromising diagnostic accuracy. This paper introduces a robust zero-watermarking technique leveraging the Collatz and Fresnelet Transforms. The Forward Collatz Transform (FCT) is initially applied to create a secure and encrypted embedding pattern for medical images. Subsequently, the Fresnelet Transform (FT) is employed, offering superior localization and frequency selectivity. From the fresnelet values, we extract two strongest Oriented FAST and Rotated BRIEF (ORB) points to enhance watermark robustness, resulting in a 64-bit perceptual image hash. Our approach adopts a dual-layer security strategy by combining FCT and Cyclic-Shift-Transformation (CST) methods, significantly fortifying the protection of watermark image data. The watermark can be efficiently extracted using the Inverse Collatz Transform (ICT). A comprehensive performance analysis evaluates our system under single, double, and multiple attacks on medical images. Our experiments clearly show that our system outperforms existing methods in medical image watermarking, demonstrating its resilience against various manipulations. This approach can significantly improve data security and reliability in medical imaging applications.

Real-time and screen-cam robust screen watermarking

Unauthorized photography of screen-loaded information has become a low-cost and difficult-to-trace method for leakage. Many researchers have proposed screen-cam robust watermarking methods for digital images and documents to identify surreptitious photography. However, these methods are designed for specific types of data and cannot be used in real time, which limits their applicability. This study proposes a screen watermarking method that possesses screen-cam robustness and real-time watermark refreshing. Specifically, a resolution-adaptive watermark template was designed that comprises message watermark templates and two types of synchronization watermark templates. To achieve real-time watermark refreshing, the pregenerated watermark template is directly embedded into the back buffer, which represents the content to be loaded onto the screen. A simplified just noticeable difference model was employed to ensure watermark imperceptibility. For watermark extraction, the positions of the embedded synchronization watermark templates are calculated using a synchronization response index method. Based on these positions, we can perform automatic perspective correction and locate the message watermark template embedded region. Finally, the message watermark was extracted from the discrete Fourier transform domain of the noise component. Extensive experiments demonstrated that the watermark refresh rate meets practical requirements and that the proposed method is robust against image-processing attacks and screen-cam attacks.

An improved reversible watermarking scheme using embedding optimization and quaternion moments

Digital watermarking of images is an essential method for copyright protection and image security. This paper presents an innovative, robust watermarking system for color images based on moment and wavelet transformations, algebraic decompositions, and chaotic systems. First, we extended classical Charlier moments to quaternary Charlier moments (QCM) using quaternion algebra. This approach eliminates the need to decompose color images before applying the discrete wavelet transform (DWT), reducing the computational load. Next, we decompose the resulting DWT matrix using QR and singular value decomposition (SVD). To enhance the system's security and robustness, we introduce a modified version of Henon's 2D chaotic map. Finally, we integrate the arithmetic optimization algorithm to ensure dynamic and adaptive watermark insertion. Our experimental results demonstrate that our approach outperforms current color image watermarking methods in security, storage capacity, and resistance to various attacks, while maintaining a high level of invisibility.

INVARIANT ZERO-WATERMARKING ALGORITHM IN DWT-DCT DOMAIN USING ROBUST FEATURES MATCHING

In order to protect the copyright of the digital contents, the robust watermarking techniques are employed based on frequency domains or combined frequency domains. Another techniques, called zero-watermarking which does not modify the original image to embed the watermark but create a watermark from its robust features, is a useful technique for resolving the tension between robustness and invisibility. In this paper, we propose a new zero-watermarking algorithm based on discrete wavelet transform and discrete cosine transform domain with significant feature points matching for improving the robustness. In our proposed method, the original image is firstly separated into three components (Y, Cr, Cb), then its Y-component is performed with discrete wavelet transform, afterwards its LL3 is transformed with discrete cosine transform. To generate the master share of the original image, the DCT-based image is binarized. After performing the Arnold transformation on the watermark, the owner share is generated by taking the XOR operation on the scrambled watermark and master share. The robustness of the our proposed algorithm for imaging processes is analyzed, and the results show that the proposed algorithm is robust to common signal processing such as noise, filtering, JPEG compression, geometric attacks such as rotation, scaling, translation and so on.