Digital Watermarking Research Articles

Enhancing copyright protection with Hadamard transform-based blind digital watermarking using the cycling chaos system

The network's ease of use has brought a copyright dilemma for some multimedia works. It is consequently necessary to develop a safe digital watermarking technology to guarantee the protection of digital work's copyright. The proposed method addresses the pressing need for robust blind digital watermarking technology to safeguard the copyrights of multimedia works in the face of the accessibility provided by modern networks. Two approaches are presented in our research paper. First, a blind Hadamard transform-based method is proposed, leveraging the energy concentration concept inherent in the transform. This technique embeds watermarks into the highest energy coefficients of color host images in the transform domain. Notably, watermark extraction is achieved without accessing the original data. Extensive simulation tests validate the efficacy of this method. Second, we have explored the burgeoning field of chaotic systems in watermarking. An LSB-based strategy, termed the cycling chaos algorithm, is introduced. This approach seamlessly integrates data, into color images using the least significant bits. It employs a cycling chaos function to generate a key for a pseudorandom number generator, which, in turn, determines the intensity and pixel positions for storing privileged data in the original image. Through simulation trials, the significant impact of this method on various performance metrics is demonstrated. These contributions aim to not only address immediate copyright protection challenges but also inspire further exploration and advancement in digital watermarking.

Ambiguity attacks on the digital image watermarking using discrete wavelet transform and singular value decomposition

Ambiguity attacks on the digital image watermarking using discrete wavelet transform and singular value decomposition

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.

Enhanced Security in Medical Imaging: A Novel Watermarking and Compression Approach

Medical images, including magnetic resonance imaging (MRI), ultrasound (US), computerized tomography (CT), X-rays, and electrocardiography (ECG), each have distinct benefits and drawbacks. Accurate identification of these images is crucial for maintaining patient-specific data integrity. This study proposes a novel watermarking technique that employs Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD) to enhance the security, confidentiality, and integrity of medical images. Previous research by Badshah et al. underlines that digital watermarking significantly bolsters the protection of medical images. Additionally, we incorporate Run Length Encoding (RLE) as a compression method to efficiently reduce data memory requirements. The implementation of these techniques demonstrated a marked improvement in the Peak Signal-to-Noise Ratio (PSNR), increasing by up to 5 dB in watermarked images compared to non-watermarked ones, indicating enhanced imperceptibility. Moreover, the file size reduction achieved through our compression approach ranged from 15% to 30%, ensuring that high-quality images consume less storage space. These advancements facilitate the secure and efficient handling of medical image data, supporting their use in clinical environments.

Multitiered Reversible Data Privacy Protection Scheme for IoT Based on Compression Sensing and Digital Watermarking

Multitiered Reversible Data Privacy Protection Scheme for IoT Based on Compression Sensing and Digital 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.

Analyzing the robustness of decentralized horizontal and vertical federated learning architectures in a non-IID scenario

Federated learning (FL) enables participants to collaboratively train machine and deep learning models while safeguarding data privacy. However, the FL paradigm still has drawbacks that affect its trustworthiness, as malicious participants could launch adversarial attacks against the training process. Previous research has examined the robustness of horizontal FL scenarios under various attacks. However, there is a lack of research evaluating the robustness of decentralized vertical FL and comparing it with horizontal FL architectures affected by adversarial attacks. Therefore, this study proposes three decentralized FL architectures: HoriChain, VertiChain, and VertiComb. These architectures feature different neural networks and training protocols suitable for horizontal and vertical scenarios. Subsequently, a decentralized, privacy-preserving, and federated use case with non-IID data to classify handwritten digits is deployed to assess the performance of the three architectures. Finally, a series of experiments computes and compares the robustness of the proposed architectures when they are affected by different data poisoning methods, including image watermarks and gradient poisoning adversarial attacks. The experiments demonstrate that while specific configurations of both attacks can undermine the classification performance of the architectures, HoriChain is the most robust one.

Techniques for Digital Watermarking in Images: A Review

Techniques for Digital Watermarking in Images: A Review

A Reversible Medical Image Watermarking Scheme for Advanced Image Tampering Detection

A Reversible Medical Image Watermarking Scheme for Advanced Image Tampering Detection

Restricted Singular Value Decomposition for a Tensor Triplet under T-Product and Its Applications

We investigate and discuss in detail the structure of the restricted singular value decomposition for a tensor triplet under t-product (T-RSVD). The algorithm is provided with a numerical example illustrating the main result. For applications, we consider color image watermarking processing with T-RSVD.

Imperceptible and Reversible Acoustic Watermarking Based on Modified Integer Discrete Cosine Transform Coefficient Expansion

This paper aims to explore an alternative reversible digital watermarking solution to guarantee the integrity of and detect tampering with data of probative importance. Since the payload for verification is embedded in the contents, algorithms for reversible embedding and extraction, imperceptibility, payload capacity, and computational time are issues to evaluate. Thus, we propose a reversible and imperceptible audio information-hiding algorithm based on modified integer discrete cosine transform (intDCT) coefficient expansion. In this work, the original signal is segmented into fixed-length frames, and then intDCT is applied to each frame to transform signals from the time domain into integer DCT coefficients. Expansion is applied to DCT coefficients at a higher frequency to reserve hiding capacity. Objective evaluation of speech quality is conducted using listening quality objective mean opinion (MOS-LQO) and the segmental signal-to-noise ratio (segSNR). The audio quality of different frame lengths and capacities is evaluated. Averages of 4.41 for MOS-LQO and 23.314 [dB] for segSNR for 112 ITU-T test signals were obtained with a capacity of 8000 bps, which assured imperceptibility with the sufficient capacity of the proposed method. This shows comparable audio quality to conventional work based on Linear Predictive Coding (LPC) regarding MOS-LQO. However, all segSNR scores of the proposed method have comparable or better performance in the time domain. Additionally, comparing histograms of the normalized maximum absolute value of stego data shows a lower possibility of overflow than the LPC method. A computational cost, including hiding and transforming, is an average of 4.884 s to process a 10 s audio clip. Blind tampering detection without the original data is achieved by the proposed embedding and extraction method.

MuST: Robust Image Watermarking for Multi-Source Tracing

In recent years, with the popularity of social media applications, massive digital images are available online, which brings great convenience to image recreation. However, the use of unauthorized image materials in multi-source composite images is still inadequately regulated, which may cause significant loss and discouragement to the copyright owners of the source image materials. Ideally, deep watermarking techniques could provide a solution for protecting these copyrights based on their encoder-noise-decoder training strategy. Yet existing image watermarking schemes, which are mostly designed for single images, cannot well address the copyright protection requirements in this scenario, since the multi-source image composing process commonly includes distortions that are not well investigated in previous methods, e.g., the extreme downsizing. To meet such demands, we propose MuST, a multi-source tracing robust watermarking scheme, whose architecture includes a multi-source image detector and minimum external rectangle operation for multiple watermark resynchronization and extraction. Furthermore, we constructed an image material dataset covering common image categories and designed the simulation model of the multi-source image composing process as the noise layer. Experiments demonstrate the excellent performance of MuST in tracing sources of image materials from the composite images compared with SOTA watermarking methods, which could maintain the extraction accuracy above 98% to trace the sources of at least 3 different image materials while keeping the average PSNR of watermarked image materials higher than 42.51 dB. We released our code on https://github.com/MrCrims/MuST

Homomorphic encryption domain asymmetric fingerprinting scheme for 3D models of oblique photography

AbstractExisting copyright protection schemes for 3D models of oblique photography predominantly utilize digital watermarking technology, most of which suffer from shortcomings such as the potential false accusation of legitimate users and the inability to effectively trace unauthorized distributors. In response to these challenges, this article proposes a homomorphic encryption domain asymmetric fingerprinting scheme for 3D models of oblique photography. Initially, the fingerprints to be embedded are collaboratively generated by the transacting parties, with the publisher remaining unaware of the user's actual fingerprint. This approach ensures the effective implementation of the asymmetric nature of the fingerprinting scheme. Subsequently, potential geometric and non‐geometric attacks are considered prior to fingerprint embedding. Robustness is enhanced for the proposed solution through relative coordinate transformations and normalization procedures. Ultimately, the embedding and extraction of fingerprints are achieved through the application of an effective bit parity check method, in conjunction with the vertex‐paradigm projection length mapping mechanism and the Paillier homomorphic encryption system, minimizing embedding distortion while ensuring data security. Experimental results demonstrate that the proposed solution exhibits satisfying robustness against common manipulations, such as cropping, simplification, and geometric attacks. Additionally, the scheme effectively safeguards legitimate users from false accusations and accurately traces maliciously authorized users.

Adversarial watermark: A robust and reliable watermark against removal

Digital image watermarking used to be an important tool for copyright protection. However, as neural network-based watermark removal methods have been proposed in recent years, the embedded watermark is increasingly easy to be erased, which poses a great threat to copyright protection. To address this issue, we propose an adversarial visible watermark scheme, which combines the visible watermark with the adversarial perturbation. By attacking the watermark removal network, we maximize the resistance of visible watermark against removal while minimizing the visual distortion. To further improve the robustness against various transformations (e.g. cropping, JPEG compression), we employ the region of interest and random pre-processing to embed the adversarial visible watermark. The experimental results show that the proposed scheme can effectively resist the removal of watermarks on different datasets and network structures while having good transferability and robustness, which enables the watermark to continue to be an effective copyright protection method.

An image watermarking scheme: combining the transform domain and deep learning modality with an improved scrambling process

ABSTRACT This paper proposes a novel image watermarking scheme with the following two processes. In the embedding process, the cover image is subjected to NSCT to obtain the properties of multi-scale and directional characteristics and fed to DWT which decomposes the contourlet image into various sub-bands. The wavelet decomposition image is subjected to a region selection step, where the regions are selected based on the DL strategy for selecting the region from the contourlet of the cover image where the features, including LOOP feature, mean feature, Canny features, and ResNet features are extracted and subjected to the DNN classifier. Conversely, the watermark image performs NSCT and DWT as the cover image and is fed into further steps to eliminate the correlation of pixels. For that, it proposes the FTTS approach to scramble the contourlet of watermark images, in which tent map and dyadic transformation are employed to eliminate the correlation among pixels. Then the resultants of region selection and FTTS are embedded to obtain the watermarked image. On the other hand, the extraction process takes place, which extracts the embedded watermarked image and applies inverse scrambling and inverse an NSCT approach to recover the watermark image against any malicious attack.

Beyond pixels and ciphers: Navigating the advancements and challenges in visual cryptography

<p>Visual cryptography (VC) has emerged as a pivotal solution for secure information transmission, leveraging its unique capability to encrypt images in a user-friendly and accessible manner. This survey paper provides an in-depth analysis of various VC methods, highlighting their distinct encryption and decryption techniques, applicability, and security levels. The study delves into the technical specifications of each VC type, offering insights into secret image formats, the number of secret images used, types of shares, pixel expansion, and complexity. Significant attention is given to the practical applications of VC, ranging from secure document verification and anti-counterfeiting measures to digital watermarking and online data protection. The paper also identifies key challenges in the field, such as image quality retention post-decryption, computational efficiency, and scalability. Future prospects of VC are explored, particularly its potential integration with emerging technologies like AI and blockchain. This survey aims to provide a comprehensive understanding of VC’s current state, its diverse applications, and the future possibilities, making it a valuable resource for researchers and practitioners in the field of data security and cryptography.</p>

Invisible Adversarial Watermarking: A Novel Security Mechanism for Enhancing Copyright Protection

Invisible watermarking can be used as an important tool for copyright certification in the Metaverse. However, with the advent of deep learning, Deep Neural Networks (DNNs) have posed new threats to this technique. For example, artificially trained DNNs can perform unauthorized content analysis and achieve illegal access to protected images. Furthermore, some specially crafted DNNs may even erase invisible watermarks embedded within the protected images, which eventually leads to the collapse of this protection and certification mechanism. To address these issues, inspired by the adversarial attack, we introduce Invisible Adversarial Watermarking (IAW), a novel security mechanism to enhance the copyright protection efficacy of watermarks. Specifically, we design an Adversarial Watermarking Fusion Model (AWFM) to efficiently generate Invisible Adversarial Watermark Images (IAWIs). By modeling the embedding of watermarks and adversarial perturbations as a unified task, the generated IAWIs can effectively defend against unauthorized identification, access, and erase via DNNs, and identify the ownership by extracting the embedded watermark. Experimental results show that the proposed IAW presents superior extraction accuracy, attack ability, and robustness on different DNNs, and the protected images maintain good visual quality, which ensures its effectiveness as an image protection mechanism.

Secure Transmission in the Digital Age: Exploring Exchange Encrypted Watermarking Technology

In an era defined by the pervasive nature of digital data and the persistent specter of cyberattacks, the imperative to safeguard sensitive information and intellectual property has ascended to a position of paramount importance. This essay embarks on a comprehensive exploration of exchange encrypted watermarking technology, a cutting-edge and highly secure methodology for data protection that seamlessly amalgamates the foundational tenets of digital watermarking and encryption. By delving deep into the concept underpinning this technology, elucidating its core principles, and elucidating its multifaceted applications, this essay seeks to illuminate the transformative potential it holds within our data-driven world. The convergence of digital watermarking and encryption not only fortifies the defenses against data breaches but also paves the way for a new era of secure data sharing, digital rights management, and content protection. This innovative approach offers a dynamic response to the evolving landscape of cyber threats, establishing itself as a potent guardian of our digital assets and a catalyst for the secure exchange of information in an increasingly interconnected global environment.

Fractional-order heterogeneous memristive Rulkov neuronal network and its medical image watermarking application

This article proposes a novel fractional heterogeneous neural network by coupling a Rulkov neuron with a Hopfield neural network (FRHNN), utilizing memristors for emulating neural synapses. The study firstly demonstrates the coexistence of multiple firing patterns through phase diagrams, Lyapunov exponents (LEs), and bifurcation diagrams. Secondly, the parameter related firing behaviors are described through two-parameter bifurcation diagrams. Subsequently, local attraction basins reveal multi-stability phenomena related to initial values. Moreover, the proposed model is implemented on a microcomputer-based ARM platform, and the experimental results correspond to the numerical simulations. Finally, the article explores the application of digital watermarking for medical images, illustrating its features of excellent imperceptibility, extensive key space, and robustness against attacks including noise and cropping.

Visual Cryptography for Color Images using Digital-Watermarking

Visual cryptography is a cryptographic technique which allows visual information (e.g. picture, texts) to be encrypted in such a way that the decryption can be performed by the human visual system, without the aid of computers. There are various measures on which performance of visual cryptography scheme depends, such as pixel expansion, contrast, security, accuracy, share generated is meaningful or meaningless, type of secret images( either binary or color) and number of secret images(either single or multiple) encrypted by the scheme. In visual cryptography encryption of image is done by dividing the image into n number of shares and decryption process is done by combining a certain number of shares or more. simple visual cryptography is not secure because of the decryption process done by visual system. The information or the image can be retrieved by anyone if the person gets at least some number of shares. Secret image can be reconstructed without any complex computation. In this project we use digital watermarking. Digital watermarking is a technique for inserting secret information into an image, which enables us to know the source or owner of the copyright