Watermark Embedding Research Articles

Transient detection-based adaptive audio watermarking using attack-aware optimization

There has been a continued need for technology to prevent the illegal use of music in online transactions and protect the rights of copyright holders. Recently, the types of attacks have become very diverse due to various online environments and content production methods. Therefore, an adaptive protection method aware of content and attacks is needed. This paper proposes a robust watermarking algorithm of a blind method to protect audio copyright in online streaming services. After detecting the transient points of the audio and processing the signals, they are used as reference triggers for frequency conversion. Transient triggers should be determined to be detected even after an attack. Audio samples are converted into the fast Fourier transform (FFT) frequency coefficients, and a watermark is embedded using the quantization index modulation (QIM) method. Various user-defined parameters can be adjusted during the watermark embedding process. Such parameters may be determined through experiments or an optimization process by targeting both the quality of the watermarked audio and the bit error rate (BER) of the watermark extracted after the attack. A watermark can be extracted using a similar process using parameters used in watermark embedding. If there is no information about the parameters used in embedding, the watermark can be extracted by tracking the minimum BER of the extracted watermark. Compared to the existing methods, the proposed watermarking technique showed excellent performance against most attacks and superior performance for filtering, shifting, and moving picture expert group (MPEG)-2 audio layer 3 (MP3) compression attacks. Therefore, the proposed method is likely a good solution for protecting audio copyright in an online streaming environment.

Visible watermarking for 3D models based on 3D Boolean operation

With the continuous expansion of the application field of 3D models and the convenience of model storage, transmission, and replication, the problems of illegal reproduction, illegal modification, and embezzlement faced by 3D models are gradually becoming severe, which has brought substantial economic losses to the producers of 3D models. Therefore, this study aims to develop a visible watermarking method for 3D model using 3D Boolean operation. First, based on TrueType font library, the 3D model of the watermark is generated through the extraction, positioning and combination of character outline. Next, the spatial registration between the watermark model and the 3D model was carried out according to the embedding position of the watermark. Finally, based on multiple 3D Boolean operations, the copyright information of the 3D watermark model is embedded into the 3D model in various modes, such as embossing, debossing, and surface mesh embedding. The experiments demonstrate that the method of this paper supports embedding watermark information in B-rep, CSG, and voxel models. This method can readily support business applications for visible watermarking of 3D models and has laid a good foundation for researching multipurpose 3D model watermarking algorithms supporting copyright notification and protection.

High-Quality Evaluation for Invisible Watermarking Based on Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD)

. In this research, we propose an innovative approach that integrates Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) to enhance the quality and security of digital images. The purpose of this technique is to embed imperceptible watermarks into images, preserving their integrity and authenticity. The integration of DCT allows for an efficient transformation of image data into frequency components, forming the basis for embedding watermarks that are nearly invisible to the human eye. In this context, SVD offers an advantage by separating singular values and corresponding vectors, facilitating a more sophisticated watermarking process. The quality evaluation using metrics such as MSE, PSNR, UQI, and MSSIM demonstrates the effectiveness of this approach. Low average MSE values, ranging from 0.0058 to 0.0064, indicate minimal distortion in the watermarked images. Additionally, high PSNR values, ranging from 67.20 dB to 67.22 dB, affirm the high image quality achieved after watermarking. These results validate that the integration of DCT and SVD provides a high level of security while maintaining optimal visual quality in digital images. This approach is highly relevant and effective in addressing the challenges of image protection in this digital era.

Robust Watermarking Algorithm for Building Information Modeling Based on Element Perturbation and Invisible Characters

With the increasing ease of building information modeling data usage, digital watermarking technology has become increasingly crucial for BIM data copyright protection. In response to the problem that existing robust watermarking methods mainly focus on BIM exchange formats and cannot adapt to BIM data, a novel watermarking algorithm specifically designed for BIM data, which combines element perturbation and invisible character embedding, is proposed. The proposed algorithm first calculates the centroid of the enclosing box to locate the elements, and establishes a synchronous relationship between the element coordinates and the watermarked bits using a mapping mechanism, by which the watermarking robustness is effectively enhanced. Taking into consideration both data availability and the need for watermark invisibility, the algorithm classifies the BIM elements based on their mobility, and perturbs the movable elements while embedding invisible characters within the attributes of the immovable elements. Then, the watermark information after dislocation is embedded into the data. We use building model and structural model BIM data to carry out the experiments, and the results demonstrate that the signal-to-noise ratio and peak signal-to-noise ratio before and after watermark embedding are both greater than 100 dB. In addition, the increased information redundancy accounts for less than 0.15% of the original data., which means watermark embedding has very little impact on the original data. Additionally, the NC coefficient of watermark extraction is higher than 0.85 when facing attacks such as translation, element addition, element deletion, and geometry–property separation. These findings indicate a high level of imperceptibility and robustness offered by the algorithm. In conclusion, the robust watermarking algorithm for BIM data fulfills the practical requirements and provides a feasible solution for protecting the copyright of BIM data.

Copyright protection of deep image classification models

With the growing number of tasks solved using deep learning methods, the need for protection against unauthorized distribution of the intellectual property such as pre-trained models of deep neural networks is growing. To date, one of the most common ways to protect copyright in the digital space is through embedding digital watermarks. When solving the problem of watermark embedding, an important criterion is the preservation of the model prediction accuracy after introducing the protective information. In this paper, we propose a method for embedding digital watermarks into image classification models based on adding images obtained by superimposing pseudo-holograms on images of the original dataset to the training set. A pseudo-hologram is an image synthesized on the basis of a given binary sequence by arranging pulses for bit encoding in the spectral region. Results of the experimental study show that the proposed method allows one to maintain the classification quality, while also retaining its performance regardless of the architecture of the protected neural network. The conducted series of attacks on protected models show that attempts of an attacker to completely remove the watermark will almost inevitably lead to a significant loss in the model prediction quality. The results of the experiments also include recommendations on the choice of method parameters, such as the size of the trigger and training sets, as well as the length of sequences encoded by pseudo-holograms.

A Fast Method for Robust Video Watermarking Based on Zernike Moments

Watermarking by Zernike moments has been proven to be effective in providing high rotational resistance. However, due to the high computational complexity, the conventional video watermarking methods using Zernike moments are developed for videos with low resolution. Moreover, according to the properties of Zernike moments, only the matrices of equal height and width can be calculated since the inscribed circle of the original image matrix is selected as the area to be processed, but most of the available videos on the Internet do not meet such requirement. To solve the above problem, this paper proposes a fast watermarking method based on Zernike moments for high resolution videos to resist various attacks. In the proposed method, the frames of a video sequence are firstly grouped, from which a certain number of frame pairs are then selected for watermark embedding. For each frame pair to be embedded, we partition one frame into a set of disjoint blocks and apply singular value decomposition to each block to obtain a square feature matrix. Thereafter, by calculating all the Zernike moments, secret information is embedded into the selected Zernike moments to achieve superior robustness while keeping imperceptibility. Finally, according to the video encoding framework, we overwrite the frame difference of the frame pair by the watermark to resist compression and transcoding attacks. Furthermore, we propose two optional methods for compensating the special cases of rotation and scaling attacks during watermark detection. Experiments demonstrate the advantage of our method over the existing robust watermarking methods.

A new highly fractal 1D- chaotic map-based novel encryption on HVS-based watermarked Fundus images for 2-level security in teleophthalmology applications

In recent years the combination of medical image watermarking and encryption is becoming popular due to a better trade-off between authenticity and security, since, each of these methods alone is insufficient to offer complete security. In the proposed work, the watermark is embedded into the Fundus image using the Singular Value Decomposition (SVD) of the La*b* space image of the Fundus image. The scaling factor is selected based on the HVS property of the human eye. The proposed embedding algorithm is semi-blind because the system requires the singular values (s) of the La*b* image at the receiver side in order to extract the watermark. To improve security, the authenticated image then undergoes 2-levels of confusion process followed by diffusion for encryption. For diffusion, the key is generated from the newly developed Modified Sine-Logistic Chaotic System (MSLCS), which is constructed by the combination of logistic map and sine map. The performance evaluation of the new chaotic system has been done in terms of the Bifurcation diagram, Lyapunov exponent, Phase portrait and Entropy. Also, Different tests such as Blood vessel segmentation and Retinal object identification were carried out on the original image, watermarked image, decrypted image and recovered image. The results show that the embedding of watermarks followed by encryption, on Fundus images does not affect the diagnosis and identification of retinal objects. Correct decryption, secured recovery of Fundus image and guaranteed extraction of the watermark at the receiver side make the proposed system secure for Fundus images in tele-ophthalmology applications.

VSTNet: Robust watermarking scheme based on voxel space transformation for diffusion tensor imaging images

Deep watermarking has been successfully applied to real-value domain (e.g., CT, MRI, DWI) medical images for copyright protection; however, manifold-value domain images, such as diffusion tensor imaging (DTI), have received less academic attention. The non-Euclidean nature of DTI images hinders deep models from generating plausible DTI images because it is difficult to guarantee that each generated DTI image voxel lies on a 3 × 3 symmetric positive definite manifold. In this paper, we propose a robust watermarking scheme based on voxel space transformation for DTI images. First, a voxel space transformation is proposed, which effectively solves the problem of fiber orientation estimation in Riemann networks for DTI images and obtains the eigenvalues and eigenvectors of each voxel using singular value decomposition (SVD) to transform the DTI voxels from the 3 × 3 symmetric positive definite manifold space to Euclidean space. Second, SVD is combined with a deep neural network to extract the high-level features of each voxel eigenvalue in the DTI images and embedding watermarking messages, avoiding the false positive errors problem in traditional watermarking algorithms based on an SVD transform. Finally, using a deep neural network combining multiscale dilated convolution, dense residual connection and channel attention, the algorithm demonstrates high robustness against various intentional or unintentional attacks on DTI images while ensuring the good visual quality and diffusion characteristics of the DTI images embedded with watermarking messages. Experiments show that the watermarking message extraction accuracy reaches 97.8% for Crop (p = 0.7) attacks and 95.4% for Gaussian Blur (k = 7) attacks.

Experimental Investigation of Protection Method for Detection of Cloning Attack on Paper Certificates

A method of paper certificate protection against a cloning attack is investigated, that was proposed recently theoretically in a paper of the same author. This method is based on the use of digital watermarks. In order to extend watermark approach to a protection against cloning attacks, it has been suggested to execute estimation of the noise power which appear during the image scanning and printing. Algorithms of embedding and extraction of watermarks out of the images are presented along with a method of detecting of the cloning attack after scanning and printing by an attacker. Numerical results of the experiments for the error probabilities of a cloning missing and a false alarm are also presented and are in agreement with theoretical results obtained before.

Counterfeiting in Depth Synthesis based on Digital Watermarking

The purpose of this paper is to discuss and apply digital watermarking technology to solve the forgery problem in depth synthesis. With the rapid development of deep synthesis technology and its application in various fields, it is particularly important to protect the authenticity and integrity of digital content. Based on the understanding of digital watermarking, this paper explores an experimental design, which uses watermarking embedding and extraction algorithms and forgery detection technology to solve the problem of deep forgery, protect the copyright, integrity and anti-copy of digital products. In order to improve the robustness and reliability of the watermark, a suitable watermark embedding and extraction algorithm is designed by analyzing the characteristics of deep synthesis forged media in the experimental process. Then select the data set containing the original digital media and the deep synthetic forged samples, extract the features of the two, and find out the features that distinguish the differences between the two. Finally, the forgery detection technology is used to evaluate the performance of digital watermarking technology in depth forgery detection. In this paper, digital watermarking technology is used to provide an effective solution to the problem of forgery in depth synthesis, which can be applied to protect intellectual property rights, prevent tampering and forgery, and protect the authenticity and integrity of digital media content.

Quantum color image watermarking scheme based on quantum error correction coding * *This work was supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province (Grant No. SKLACSS–202208), the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJZD-M202000501) and National Natural Science Foundation of China (No. 61772295).

Quantum image processing, which merges classical image processing techniques with quantum computing, provides exceptional storage capacity and unparalleled parallel computing power. In this study, we present a quantum color image watermarking scheme that employs quantum error correction codes to address issues such as pixel loss and image distortion during watermark embedding and extraction. By utilizing the least significant bit method to embed the color values of the watermark image into those of the carrier image, we improve the scheme’s robustness. We also address the error correction capabilities of channel coding for phase-flip errors and follow the majority principle, resulting in more accurate extraction of the watermark image’s color and enhancing the watermarking scheme’s reliability and integrity. Our experimental simulations demonstrate that the proposed watermarking scheme boasts high security, strong robustness, and excellent concealment.

Adaptor: Improving the Robustness and Imperceptibility of Watermarking by the Adaptive Strength Factor

In watermarking, the watermark embedding strength is crucial, and the introduction of the strength factor can adjust the trade-off between the quality of the encoded image and the accuracy of the recovered message, thus enabling good imperceptibility and robustness of the encoded image. In traditional watermarking methods, the strength factor is selected in relation to the cover image, and based on different images, different strength factors are manually selected or algorithmically derived to adjust the visual effect of the watermarked image. However, due to the subjectivity and inflexibility of traditional algorithms, they can not achieve the effect of adaptive adjustment of watermarked images. Recently, watermarking methods combined with deep learning have gradually occupied the mainstream of this field. In the testing stage, to balance the overall robustness and imperceptibility, the strength factor is no longer selected based on the cover image as in traditional methods. Instead, it is set to a universal value. Therefore, the watermarking method based on deep learning is still in the primary stage of the trial-and-error method. To solve the subjectivity of the hand-designed embedding strength algorithm of the traditional watermarking methods and the low elasticity of the strength factor of the learning method so as to realize the adaptive embedding of watermarks, we propose an adaptive watermarking method with separate training. The proposed method adds a new component, the Adaptor, compared to other frameworks. The Adaptor can adaptively select strength factors to control the embedding strength of the watermark relying on the cover image and secret message. A two-stage training method is used to maintain the stability of the training and to achieve the best results for each component. With the results obtained from our experiments, our proposed method can find the appropriate strength factor and optimize it, resulting in improved robustness and imperceptibility of the watermark. The proposed method shows better results compared to the current state-of-the-art algorithms.

A Robustness-Assured White-Box Watermark in Neural Networks

Recently, stealing highly-valuable and large-scale deep neural network (DNN) models becomes pervasive. The stolen models may be re-commercialized, e.g., deployed in embedded devices, released in model markets, utilized in competitions, etc, which infringes the Intellectual Property (IP) of the original owner. Detecting IP infringement of the stolen models is quite challenging, even with the white-box access to them in the above scenarios, since they may have experienced fine-tuning, pruning, functionality-equivalent adjustment to destruct any embedded watermark. Furthermore, the adversaries may also attempt to extract the embedded watermark or forge a similar watermark to falsely claim ownership. In this paper, we propose a novel DNN watermarking solution, named <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$HufuNet$</tex-math></inline-formula> , to detect IP infringement of DNN models against the above mentioned attacks. Furthermore, HufuNet is the first one theoretically proved to guarantee robustness against fine-tuning attacks. We evaluate HufuNet rigorously on four benchmark datasets with five popular DNN models, including convolutional neural network (CNN) and recurrent neural network (RNN). The experiments and analysis demonstrate that HufuNet is highly robust against model fine-tuning/pruning, transfer learning, kernels cutoff/supplement, functionality-equivalent attacks and fraudulent ownership claims, thus highly promising to protect large-scale DNN models in the real world.

PROTECTION OF POLIGRAPHIC PRODUCTS USING DIGITAL WATERMARKS

The article proposes algorithms for steganographic embedding and extraction of digital watermarks (DWM)&#x0D; embedded in the digital original of printed products in order to protect copyrights to them. The built-in digital&#x0D; image is resistant to distortions introduced into images during printing and scanning and is not visually noticeable.&#x0D; The resistance of the extraction algorithm to geometric distortions is achieved through the use of an algorithm&#x0D; for restoring the original geometry of the image by comparing the special points of the distorted copy of the container&#x0D; image and the original image. The results of testing the robustness of the algorithm both to simulated distortions&#x0D; of the printing and scanning channel, and to distortions introduced into images when using real printing and scanning&#x0D; devices are presented. The impact of the embedding process on image quality is also considered.

A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking

This paper presents a comprehensive survey of deep learning-based image watermarking; this technique entails the invisible embedding and extraction of watermarks within a cover image, aiming for a seamless combination of robustness and adaptability. We navigate the complex landscape of this interdisciplinary domain, linking historical foundations, current innovations, and prospective developments. Unlike existing literature, our study concentrates exclusively on image watermarking with deep learning, delivering an in-depth, yet brief analysis enriched by three fundamental contributions. First, we introduce a refined categorization, segmenting the field into embedder–extractor, deep networks for feature transformation, and hybrid methods. This taxonomy, inspired by the varied roles of deep learning across studies, is designed to infuse clarity, offering readers technical insights and directional guidance. Second, our exploration dives into representative methodologies, encapsulating the diverse research directions and inherent challenges within each category to provide a consolidated perspective. Lastly, we venture beyond established boundaries, outlining emerging frontiers and providing detailed insights into prospective research avenues.

A chaotic hierarchical encryption/watermark embedding scheme for multi-medical images based on row–column confusion and closed-loop bi-directional diffusion

Security during remote transmission has been an important concern for researchers in recent years. In this paper, a hierarchical encryption multi-image encryption scheme for people with different security levels is designed, and a multi-image encryption (MIE) algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper. While ensuring secure communication of medical image information, people with different security levels have different levels of decryption keys, and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys. The highest security level can obtain decrypted images without watermarks, and at the same time, patient information and copyright attribution can be verified by obtaining watermark images. The experimental results show that the scheme is sufficiently secure as an MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works.

A two-stage robust reversible watermarking using polar harmonic transform for high robustness and capacity

Designing the robust reversible watermarking (RRW) with high robustness and capacity while maintaining invisibility and reversibility is a challenging problem. To alleviate this problem, we present a two-stage RRW scheme using the polar harmonic transform (PHT). In the first stage, it selects robust PHT moments for robust watermarking, develops an adaptive normalization strategy, and constructs a robust watermark embedding method. Specifically, the PHT moments with high robustness are first chosen via experimental simulations. The adaptive normalization strategy is then developed to adjust the robust watermark's embedding strength for the selected moments, aiming at achieving higher robustness. By adjusting quantized errors to integers, the standard distortion-compensated quantization index modulation (DC-QIM) is optimized. And the optimized DC-QIM for embedding robust watermark facilitates decreasing the number of bits of quantized errors representation and thus increasing the embedding capacity. In the second stage, the reversible watermarking is conducted to achieve reversibility in the absence of attacks. That is, distortions caused by the robust watermark embedding are inserted by a reversible watermarking technique. The proposed scheme performs better than state-of-the-arts in terms of bit error rate (BER) performance, according to extensive experimental results. Our code is available at https://github.com/yichao-tang/PHT-RRW.

An edge inspired image watermarking approach using compass edge detector and LSB in cybersecurity

In the contemporary era, data holds a vital significance, and ensuring the security of data stands as an exceptionally challenging endeavor. Both industry and academia dedicate substantial efforts to mitigate security breaches. Given the evolving nature of modern cyber threats, there arises a compelling necessity for robust cybersecurity measures concerning digital content. We propose the merits of digital watermarking within the realm of cybersecurity for digital content. Researchers within the watermarking domain are diligently striving to offer the utmost security services. Unfortunately, achieving both robustness and security concurrently within these techniques has proven to be a formidable task. Hence, we delve into a novel approach for image watermarking, which draws inspiration from gradient-based methodologies. This scheme hinges upon compass edge detection, the least significant bit mechanism, and the utilization of chaotic encryption for watermark embedding. The effectiveness and performance are rigorously evaluated against a spectrum of geometrical and image-processing attacks.

A novel color image tampering detection and self-recovery based on fragile watermarking

In industrial applications, sensing devices' image data must be accurately identified and recovered to prevent economic losses. Nevertheless, most industrial image watermarking methods focus on grayscale images with difficulty in achieving superior performance in three parameters: watermarked image quality, tamper detection and content recovery quality. To resolve this issue, a color image tampering detection and self-recovery method based fragile watermarking scheme is proposed in this paper. In the proposed scheme, block-based regular marker with pixel-based continuous marker is used for watermark embedding to enhance the quality of watermarked images. Then, a feature extraction-based tampering detection scheme for diagonal blocks combined with three-layer authentication is developed to resist tampering attacks of diverse shapes and sizes. Additionally, the block pixel-level recovery mechanism along with the Smoothing Inpainting algorithm is designed to implement the high-quality recovery of tampered images. Compared with recent methods, experimental results demonstrate the superiority of the scheme on tamper detection and image self-recovery, in terms of the image recovery robustness, tamper localization and attack resistance. Our scheme has a PSNR value of 25.4921 dB and an SSIM value of 0.7728 for the recovered image at 90% tampering rate.

Universal screen-shooting robust image watermarking with channel-attention in DCT domain

In this paper, we propose a universal screen-shooting robust image watermarking scheme, which can be used to embed extractable information into on-screen images for copyright protection or additional information acquisition. Specifically, to obtain higher robustness and watermarked image quality, we introduce the channel-attention mechanism into DCT domain and exploit the noise layer to guide the watermarking model, which integrates simulated distortions and screen-shooting distortions. After training, the universal watermark mask can be directly added on arbitrary cover image to produce a watermarked image that effectively resists multiple distortions. Experimental results show that the proposed scheme can achieve better performance than some state-of-the-art (SOTA) schemes in terms of the watermark embedding invisibility and the watermark extraction accuracy.