Watermarking Scheme Research Articles

A Complex Network Analysis of Image Watermarking Scheme Based on SVD and DWT

A Complex Network Analysis of Image Watermarking Scheme Based on SVD and DWT

Color image watermarking using vector SNCM-HMT

An image watermarking scheme is typically evaluated using three main conflicting characteristics: imperceptibility, robustness, and capacity. Developing a good image watermarking method is challenging because it requires a trade-off between these three basic characteristics. In this paper, we proposed a statistical color image watermarking based on robust discrete nonseparable Shearlet transform (DNST)-fast quaternion generic polar complex exponential transform (FQGPCET) magnitude and vector skew-normal-Cauchy mixtures (SNCM)-hidden Markov tree (HMT). The proposed watermarking system consists of two main parts: watermark inserting and watermark extraction. In watermark inserting, we first perform DNST on R, G, and B components of color host image, respectively. We then compute block FQGPCET of DNST domain color components, and embed watermark signal in DNST-FQGPCET magnitudes using multiplicative approach. In watermark extraction, we first analyze the robustness and statistical characteristics of local DNST-FQGPCET magnitudes of color image. We then observe that, vector SNCM-HMT model can capture accurately the marginal distribution and multiple strong dependencies of local DNST-FQGPCET magnitudes. Meanwhile, vector SNCM-HMT parameters can be computed effectively using variational expectation–maximization (VEM) parameter estimation. Motivated by our modeling results, we finally develop a new statistical color image watermark decoder based on vector SNCM-HMT and maximum likelihood (ML) decision rule. Experimental results on extensive test images demonstrate that the proposed statistical color image watermarking provides a performance better than that of most of the state-of-the-art statistical methods and some deep learning approaches recently proposed in the literature.

Scalable watermarking for identifying large language model outputs

Large language models (LLMs) have enabled the generation of high-quality synthetic text, often indistinguishable from human-written content, at a scale that can markedly affect the nature of the information ecosystem1–3. Watermarking can help identify synthetic text and limit accidental or deliberate misuse4, but has not been adopted in production systems owing to stringent quality, detectability and computational efficiency requirements. Here we describe SynthID-Text, a production-ready text watermarking scheme that preserves text quality and enables high detection accuracy, with minimal latency overhead. SynthID-Text does not affect LLM training and modifies only the sampling procedure; watermark detection is computationally efficient, without using the underlying LLM. To enable watermarking at scale, we develop an algorithm integrating watermarking with speculative sampling, an efficiency technique frequently used in production systems5. Evaluations across multiple LLMs empirically show that SynthID-Text provides improved detectability over comparable methods, and standard benchmarks and human side-by-side ratings indicate no change in LLM capabilities. To demonstrate the feasibility of watermarking in large-scale-production systems, we conducted a live experiment that assessed feedback from nearly 20 million Gemini6 responses, again confirming the preservation of text quality. We hope that the availability of SynthID-Text7 will facilitate further development of watermarking and responsible use of LLM systems.

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.

Bivariate BMM-based hybrid domain image watermark detector

Robustness, imperceptibility, and watermark capacity are three indispensable and contradictory properties for any image watermarking systems. It is a challenging work to achieve the balance among the three important properties. In this paper, by using the bivariate Beta mixture model (Bivariate BMM), we present a statistical image watermark scheme in nonsubsampled Contourlet transform (NSCT)-fractional order Jacobi Fourier moments (FJFMs) amplitude hybrid domain. The whole watermarking algorithm includes two parts: watermark embedding and detection. NSCT is firstly performed on host image to obtain the frequency subbands, and the NSCT subbands are divided into non overlapping blocks. Then, the significant NSCT domain blocks are selected using local binary patterns (LBP). Meanwhile, for each selected NSCT coefficient block, FJFMs are calculated to obtain the NSCT-FJFMs amplitude. Finally, watermark signals are inserted into the amplitude hybrid domain of NSCT-FJFMs. In order to detect accurately watermark signal, the statistical characteristics of NSCT-FJFMs magnitudes are analyzed in detail. Then, NSCT-FJFMs magnitudes are described statistically by Bivariate BMM, which can simultaneously capture the marginal distribution and strong dependencies of NSCT-FJFMs magnitudes. Also, Bivariate BMM parameters are estimated accurately by the rough-enhanced-Bayes mixture estimation & expectation–maximization (REBMIX&EM) approach. Finally, a statistical watermark detector based on the locally optimum (LO) decision rule and Bivariate BMM is developed in NSCT-FJFMs magnitude hybrid domain. Also, the closed-form expressions on the LO statistic is derived and the receiver operating characteristic (ROC) about our detector is analyzed. Extensive experimental results show the superiority of the proposed image watermark detector over some state-of-the-art statistical watermarking methods.

A Nested Digital Watermarking Scheme Using GARN with DWT

A Nested Digital Watermarking Scheme Using GARN with DWT

Rotation Correction Algorithm Based on Polar Harmonic Fourier Moments and Optimization of Color Image Security Quantization Watermarking Scheme

Rotation Correction Algorithm Based on Polar Harmonic Fourier Moments and Optimization of Color Image Security Quantization Watermarking Scheme

Novel Robust Video Watermarking Scheme Based on Concentric Ring Subband and Visual Cryptography With Piecewise Linear Chaotic Mapping

Novel Robust Video Watermarking Scheme Based on Concentric Ring Subband and Visual Cryptography With Piecewise Linear Chaotic Mapping

An Efficient Cryptographic Scheme based on Optimized Watermarking Scheme for Securing Internet of Things

In this work, a new efficient cryptographic scheme based on the concept of chaotic map and optimized watermarking scheme is proposed. In the optimized watermarking scheme, a combination of discrete wave transformation (DWT), hessenberg decomposition (HD), and singular value decomposition (SVD) are used. In this, the host image is first broken down into several sub-bands using multi-level DWT, and the resulting coefficients are then fed into HD during the embedding phase. Simultaneous watermark operation is performed on SVD. Finally, the scale factor embeds the watermark into the host image. The Differential evolution method is used to find the best scaling factor for the optimized watermarking scheme. The resulting watermarked image is then encrypted by the session key based scheme. In this scheme for each image encryption, a new random session key will be produced. The presented approach uses 64-bit plaintext and a variable size key that will be decided at the time of encryption for encrypting an image. Since session keys change with each transmission, this approach does not involve extracting and remembering session keys in order to produce subsequent session keys. IoT devices are used to test the developed method for security. The experiment’s findings shows that the suggested method works better than the current scheme in several aspects.

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

Enhancing medical image security through machine learning and dual watermarking-based technique

ABSTRACT As the world becomes increasingly digital, healthcare is no exception. With the ease of sharing e-healthcare records over open networks, smart healthcare systems have become a popular way to manage patient information. But as the popularity of these systems has grown, so has the concern for their security. That's where image security techniques come in. In this paper we have developed a new approach to secure e-patient records like DICOM images. By combining the redundant discrete wavelets transform (RDWT), Hessenberg Decomposition (HD), and randomized singular value decomposition (RSVD). We developed a robust and dual watermarking scheme. This scheme uses multiple watermarks, including Electronic Patient Record (EPR) as text and images, to ensure high-level authentication. In order to attain a balance between imperceptibility and robustness, a PSO-based optimization of scale factor is employed and Turbo code is utilized to encode the EPR and minimize channel noise. Additionally, the marked image undergoes encryption using a 3D chaotic-based encryption technique, and the extracted watermark is denoised through a deep neural network. The result shows that the proposed scheme is both secure and reliable. With this dual watermarking scheme, we have made great strides in securing e-patient records.

Fast and robust JND-guided video watermarking scheme in spatial domain

Generally speaking, those watermarking studies using the spatial domain tend to be fast but with limited robustness and imperceptibility while those performed in other transform domains are robust but have high computational cost. Watermarking applied to digital video has as one of the main challenges the large amount of computational power required due to the huge amount of information to be processed. In this paper we propose a watermarking algorithm for digital video that addresses this problem. To increase the speed, the watermark is embedded using a technique to modify the DCT coefficients directly in the spatial domain, in addition to carrying out this process considering the video scene as the basic unit and not the video frame. In terms of robustness, the watermark is modulated by a Just Noticeable Distortion (JND) scheme computed directly in the spatial domain guided by visual attention to increase the strength of the watermark to the maximum level but without this operation being perceivable by human eyes. Experimental results confirm that the proposed method achieves remarkable performance in terms of processing time, robustness and imperceptibility compared to previous studies.

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.

Commutative Encryption and Reversible Watermarking Algorithm for Vector Maps Based on Virtual Coordinates

The combination of encryption and digital watermarking technologies is an increasingly popular approach to achieve full lifecycle data protection. Recently, reversible data hiding in the encrypted domain (RDHED) has greatly aroused the interest of many scholars. However, the fixed order of first encryption and then watermarking makes these algorithms unsuitable for many applications. Commutative encryption and watermarking (CEW) technology realizes the flexible combination of encryption and watermarking, and suits more applications. However, most existing CEW schemes for vector maps are not reversible and are unsuitable for high-precision maps. To solve this problem, here, we propose a commutative encryption and reversible watermarking (CERW) algorithm for vector maps based on virtual coordinates that are uniformly distributed on the number axis. The CERW algorithm consists of a virtual interval step-based encryption scheme and a coordinate difference-based reversible watermarking scheme. In the encryption scheme, the map coordinates are moved randomly by multiples of virtual interval steps defined as the distance between two adjacent virtual coordinates. In the reversible watermarking scheme, the difference expansion (DE) technique is used to embed the watermark bit into the coordinate difference, computed based on the relative position of a map coordinate in a virtual interval. As the relative position of a map coordinate in a virtual interval remains unchanged during the coordinate scrambling encryption process, the watermarking and encryption operations do not interfere with each other, and commutativity between encryption and watermarking is achieved. The results show that the proposed method has high security, high capacity, and good invisibility. In addition, the algorithm applies not only to polyline and polygon vector data, but also to sparsely distributed point data, which traditional DE watermarking algorithms often fail to watermark.

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.

LƯỢC ĐỒ THỦY VÂN DỰA VÀO BIT Ý NGHĨA NHẤT ĐỂ BẢO VỆ BẢN QUYỀN CÔNG KHAI CHO CÁC CƠ SỞ DỮ LIỆU QUAN HỆ

The paper presents a watermarked scheme based on the most significant bit for public copyright protection for relational databases. A Watermark scheme could openly prove data copyright as often as desired. This watermark scheme is stable against common attacks such as adding, editing, and deleting data values randomly or selectively. Keywords: Public copyright protection; watermark; relation database.

An optimized watermarking scheme based on genetic algorithm and elliptic curve

Digital watermarking serves as a crucial tool for tracing copyright infringements and ensuring the authenticity and integrity of sensitive information. The fundamental concept involves embedding a watermark in the host information, ensuring its undetectability by unauthorized parties. The efficacy of a watermarking scheme mainly depends on achieving high levels of imperceptibility, robustness, and embedding capacity. These attributes are intricately linked to both the selection of the host information segment and the embedding factor. Existing schemes often (i) employ the entire host information for embedding, incurring computational expenses, and (ii) optimize the embedding factor without considering imperceptibility, robustness, and embedding capacity simultaneously, resulting in less secure watermarks. To address these limitations, we introduce a novel watermarking scheme leveraging elliptic curves (ECs) and genetic algorithms (GA). We model the choice of the embedding part by generating pseudo-random numbers over ECs, taking advantage of their proven sensitivity, security, and low computational complexity. Due to parallel search and adaptability to non-linear relationships of GA, the scheme employs genetic optimization with a multivariate objective function to establish a balance between imperceptibility, robustness, and embedding capacity for optimal watermarked generation. Rigorous analysis and comparisons demonstrate that our proposed scheme attains significantly higher imperceptibility, robustness, and embedding capacity compared to existing optimized schemes. Furthermore, our scheme exhibits a speed advantage, being up to 278 and 21 times faster than optimized and non-optimized schemes, respectively, thereby affirming its practical applicability.

Digital Watermarking Using Hybrid Grasshopper Optimization Algorithm and Genetic Algorithm (HGOAGA)

The advancement of computer technology has raised significant issues with digital content piracy and copyright law. A popular method of protecting copyright and related uses is digital watermarking. Various algorithms have been developed to address the need for invisible performance and robustness in digital watermarking schemes. Here, we proposed a novel evolutionary algorithm, the hybrid Grass Hopper Optimization algorithm, and the Genetic algorithm (HGOAGA) for optimizing multiple scaling factors in the digital watermarking scheme in the frequency domain of hybrid Discrete Wavelet Transformation (DWT) and Singular Value Decomposition (SVD) method (hybrid DWT-SVD). The subcomponents of the image are determined by calculating the DWT of the cover image. The problem is determining the best scaling factor for watermarking after converting the subcomponent to the frequency domain using SVD. In HGOAGA, an optimal solution of multiple scaling factors is found after several iterations, starting with a set of randomly generated solutions. The advantages of GOA and GA are combined in the HGOAGA to balance exploration and exploitation functionalities. Furthermore, HGOAGA can converge quickly and escape local optima well. Some standard images were used in the MATLAB environment to test the proposed algorithm. The evaluation of the experiment was carried out using various metrics such as the Structural Similarity Index (SSIM), the Normalized Cross-Correlation (NCC), and the Peak Signal-to-Noise Ratio (PSNR). The experimental results of the tests showed a PSNR value of 51db for the proposed method compared to existing methods, and they are best suited to solving conflict problems between robustness and quality.

Multi-image based self-embedding watermarking with lossless tampering recovery capability

In recent years, many self-embedding watermarking schemes have been proposed to protect the integrity of images and recover the tampered images. However, current self-embedding watermarking schemes cannot simultaneously achieve the lossless recovery and high tampering-resistance ability. In some sensitive fields, e.g., the medical field and the military field, the lossless recovery and high tampering-resistance ability are extremely important. Therefore, we propose a multi-image based self-embedding watermarking scheme with lossless tampering recovery capability (MISEW), which can not only realize the integrity authentication but also achieve the lossless recovery of the tampered content, even if the watermarked images are tampered extensively. Specifically, the image owner first uses the secret-sharing system and the reference-sharing mechanism to generate multiple sub-secret keys and watermarked images, respectively. Then, these images are sent to multiple receivers. At the receiving end, a single receiver can only realize integrity authentication and tampering localization according to a sub-secret key generated by the secret-sharing system. In addition, only when the number of cooperating receivers exceeds a threshold, the receivers can recover the complete-secret key, and then recover the original image without any loss. Experimental results show that even if all watermarked images are tampered by 44%, our MISEW scheme can achieve lossless recovery. Besides, as long as no less than two watermarked images avoid tampering, lossless recovery can be achieved regardless of the tampering degree for other watermarked images.

Retraction Note: A copyright protection using watermarking scheme based on nonlinear permutation and its quality metrics

Retraction Note: A copyright protection using watermarking scheme based on nonlinear permutation and its quality metrics