Watermark Embedding Process Research Articles

Hadamard Error-Correcting Codes and Their Application in Digital Watermarking.

In communication technologies such as digital watermarking, wireless sensor networks (WSNs), and visual light communication (VLC), error-correcting codes are crucial. The Enhanced Hadamard Error-Correcting Code (EHC), which is based on 2D Hadamard Basis Images, is a novel error correction technique that is presented in this study. This technique is used to evaluate the effectiveness of the video watermarking scheme. Even with highly sophisticated embedding techniques, watermarks usually fail to resist such comprehensive attacks because of the extraordinarily high compression rate of approximately 1:200 that is frequently employed in video dissemination. It can only be used in conjunction with a sufficient error-correcting coding method. This study compares the efficacy of the well-known Reed-Solomon Code with this novel technique, the Enhanced Hadamard Error-Correcting Code (EHC), in maintaining watermarks in embedded videos. The main idea behind this newly created multidimensional Enhanced Hadamard Error-Correcting Code is to use a 1D Hadamard decoding approach on the 2D base pictures after they have been transformed into a collection of one-dimensional rows. Following that, the image is rebuilt, allowing for a more effective 2D decoding procedure. Using this technique, it is possible to exceed the theoretical error-correcting capacity threshold of ⌊dmin-12⌋ bits, where dmin is the Hamming distance. It may be possible to achieve better results by converting the 2D EHC into a 3D format. The new Enhanced Hadamard Code is used in a video watermarking coding scheme to show its viability and efficacy. The original video is broken down using a multi-level interframe wavelet transform during the video watermarking embedding process. Low-pass filtering is applied to the video stream in order to extract a certain frequency range. The watermark is subsequently incorporated using this filtered section. Either the Reed-Solomon Correcting Code or the Enhanced Hadamard Code is used to protect the watermarks. The experimental results show that EHC far outperforms the RS Code and is very resilient against severe MPEG compression.

High-Frequency Artifacts-Resistant Image Watermarking Applicable to Image Processing Models

With the extensive adoption of generative models across various domains, the protection of copyright for these models has become increasingly vital. Some researchers suggest embedding watermarks in the images generated by these models as a means of preserving IP rights. In this paper, we find that existing generative model watermarking introduces high-frequency artifacts in the high-frequency spectrum of the marked images, thereby compromising the imperceptibility and security of the generative model watermarking system. Given this revelation, we propose an innovative image watermarking technology that takes into account frequency-domain imperceptibility. Our approach abandons the conventional convolutional neural network (CNN) structure typically used as the watermarking embedding network in popular watermarking techniques. This helps the image watermarking system to avoid the inherent high-frequency artifacts commonly produced by CNNs. In addition, we design a frequency perturbation generation network to generate low-frequency perturbations. These perturbations are subsequently added as watermarks to the low-frequency components of the carrier image, thus minimizing the impact of the watermark embedding process on the high-frequency properties of the image. The results show that our proposed watermarking framework can effectively embed low-frequency perturbation watermarks into images and effectively suppress high-frequency artifacts in images, thus significantly improving the frequency-domain imperceptibility and security of the image watermarking system. The introduced approach enhances the average invisibility performance in the frequency domain by up to 24.9% when contrasted with prior methods. Moreover, the method attains superior image quality (>50 dB) in the spatial domain, accompanied by a 100% success rate in watermark extraction in the absence of attacks. This underscores its capability to uphold the efficacy of the protected network and preserve the integrity of the watermarking process. It always maintains excellent imperceptibility and robustness. Thus, the framework shows great potential as a state-of-the-art solution for protecting intellectual property.

A robust blind color watermarking algorithm based on the Radon-DCT transform

Digital watermarking is an effective technique for image copyright protection. Many digital image watermarking algorithms are sensitive to geometric distortions. These distortions make it difficult to detect and extract watermarks. In this study, a robust color watermarking algorithm combining the Radon transform and DCT transform is proposed. First, the color carrier image is converted to the YUV color space from RGB, and the U component of YUV is transformed to the Radon domain using the Radon transform. Then, a two-dimensional 8 × 8 discrete cosine transform (2D-DCT) is carried out on the selected blocks of the Radon domain, and some fixed midfrequency coefficients are selected to embed watermark information. Moreover, the Arnold transform is applied to encrypt the watermarks and the random permutation function to scramble the embedding positions. Finally, the watermark embedding and blind extraction processes are completed by modifying the midfrequency coefficients using the presented rules. A series of simulation experiments show that the watermark algorithm has high imperceptibility and good robustness to various attacks.

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.

An effective digital image watermarking scheme incorporating DCT, DFT and SVD transformations.

Image watermarking prevents copyright infringements by attaching visible/invisible watermark images as authentication identities in the owner's documents. The article made analysis on the advantages of different transformations for choosing better combinations to make the watermark embedding process and observed that watermarking techniques incorporating discrete cosine transform (DCT) provide better resistance towards JPEG based potential attacks, discrete Fourier transform (DFT) has strong energy compaction with geometrical invariance properties to resist geometric attacks while singular value decomposition (SVD) provides stability, proportion invariance and rotation invariance properties and it provides strong resistance against noise based attacks. Considering these advantages of different transformations, the article presents a new non-blind watermarking algorithm by utilizing advantages of DFT, DCT and SVD transforms while attaching secret contents in cover images. The algorithm starts by applying DFT followed by onion peel decomposition (OPD) for decomposing Fourier domain carrier image to four frequency sub images. The scheme then applies DCT on the frequency bands and orders them in zigzag fashion to form four individual frequency arrays. In the final step of embedding process, it embeds four copies of watermark singular value contents in DFT domain with the carrier image singular value contents to produce the watermarked image. The experimental results based on subjective and objective metrics on various test images from standard image databases with different test conditions demarcate the stability of new algorithm in producing high quality watermarked images with fewer distortions even when the watermarked images are extremely distorted by potential attacks.

Dynamically selected watermark insertion using machine learning

Watermarking is a process of embedding one image into another image, used to protect the image from copyright infringement; the main aim of this watermarking is to provide ownership assertion- only rightful owner can extract the watermark from image to prove the ownership. Day-to-day the attacks on digital content (like images) are increasing, but still the same traditional watermarking process is used to for content protection. In traditional watermark embedding process static watermarks are used at the source which may lead to compromise of algorithm. To avoid this, in this paper a very new way of watermarking is introduced with the help of object detection, along with the concept of content-related watermarking by dynamically selecting the watermark image.

A Self-Error-Correction-Based Reversible Watermarking Scheme for Vector Maps

The existing digital watermarking schemes for vector maps focus mainly on the process of watermark embedding, while few works have been conducted on the topic of the self-optimization of watermark data in the process of watermark detection. There is thus still much room for accuracy improvement in watermark detection. In this paper, a model of mixed watermark data construction is built first. It constructs the error-correction codes and checking code of the original copyright watermark data and combines them to generate the final watermark data. Additionally, a lossless compression algorithm is designed for watermark data to constrain the total watermark length. Based on the constructed model, a self-error-correction-based reversible watermarking scheme is put forward for vector maps. In this scheme, map vertices are divided into non-intersecting groups first according to stability, and mixed watermark data are then embedded with respective vertex groups. Simulation results demonstrate that the watermark capacity of this scheme is 1.0000, the coordinate error caused by the watermark embedding process can be limited to no more than 0.00001 when the strength of watermark embedding is set to five, and several watermark bits can be effectively detected and corrected after watermark extraction. Experimental results and analysis show that it can strike a good balance among reversibility, invisibility, capacity and robustness. It can provide a novel solution to improve the watermark detection accuracy of digital watermarking schemes for vector maps.

Robust Watermarking Scheme for Vector Geographic Data Based on the Ratio Invariance of DWT–CSVD Coefficients

Traditional frequency-domain watermarking algorithms for vector geographic data suffer from disadvantages such as the random watermark embedding position, unpredictable embedding strength, and difficulty in resisting multiple attacks at the same time. To address these problems, we propose a novel watermarking algorithm based on the geometric invariance of the ratios of discrete wavelet transform (DWT) and complex singular value decomposition (CSVD) coefficients, which embeds the watermark information in a new embedding domain. The proposed scheme first extracts feature points from the original vector geographic data using the Douglas–Peucker algorithm, and then constructs a complex sequence based on the feature points set. The two-level DWT is then performed on the complex sequence to obtain the low frequency coefficients (L2) and high frequency coefficients (H2). On this premise, the CSVD algorithm is utilized to calculate the singular values of L2 and H2, and the ratio of the singular values of L2 and H2 is acquired as the watermark embedding domain. During the watermark embedding process, a new watermark sequence is created by the fusion of the original watermark index value and bits value to improve the recognition of the watermark information, and the decimal part at different positions of the ratio is altered by the new watermark sequence to control the watermark embedding strength. The experimental results show that the proposed watermarking algorithm is not only robust to common attacks such as geometric, cropping, simplification, and coordinate point editing, but also can extract watermark images with a high probability under random multiple attacks.

Data Protection in Palmprint Recognition via Dynamic Random Invisible Watermark Embedding

Palmprint recognition is one of the most popular biometric technologies. Recent researches mainly focus on the recognition performance, while pay less attention to the data protection issues. In this paper, we propose an active biometric data protection model for securing palmprint images in transmission or storage scenarios, called Dynamic Random Invisible Watermark Embedding (DRIWE) model. The DRIWE model implicitly embeds a watermark in each original palmprint ROI image, and then separates the embedded watermark from the watermarked image before identification. If the separated watermark is consistent with the original watermark, it indicates that the image is trustworthy and can be used in the subsequent recognition process. Otherwise, it proves that the image has been illegally tampered with. Furthermore, a two-dimensional image information entropy loss is proposed to enhance the generalization of the model to different watermarks. It ensures that the model can always assign enough information to the host image (i.e., original palmprint image) when different watermarks are applied. Thus, it enables the separator to extract the complete watermark from the watermarked image. This greatly enhances the dynamics and randomness of the watermark embedding process and further improves the ability to secure the data. Adequate experiments are conducted on two benchmark palmprint databases. The results show that the proposed DRIWE model has satisfactory attack resistance and strong generalization ability: even if only one watermark is used in training stage, it can be generalized to a dozen of other new watermark images in the testing stage. In addition, the optimal accuracy of the watermarked data is only reduced by 0.07% compared with the original data.

BGGMM-HMT based locally optimum image watermark detector in high-order NSST difference domain

Imperceptibility, robustness and data payload are three main requirements of any image watermarking systems to guarantee desired functionalities, but there is a tradeoff among them from the information-theoretic perspective. How to achieve this balance is a major challenge. In this paper, we propose a new statistical image watermarking scheme, which is based on the high-order difference coefficients in nonsubsampled Shearlet transform (NSST) domain and the bounded generalized Gaussian mixture model-based hidden Markov tree (BGGMM-HMT). In the watermark embedding process, we use a nonlinear embedding approach to hide the digital watermark into the robust high-order difference coefficients, which can achieve better imperceptibility. In the watermark detection process, high-order difference coefficients are accurately modeled by using BGGMM-HMT, where the distribution characteristics of high-order difference coefficients can be captured through BGGMM, and the scale dependencies of high-order difference coefficients can be captured through HMT. Statistical model parameters are then estimated by combining the approach of minimizing the higher bound on data negative log-likelihood function and upward–downward algorithm. Finally, an image watermark detector based on BGGMM-HMT is developed using the locally optimum (LO) decision rule. For the proposed detector, the receiver operating characteristic (ROC) expression is derived in detail. We evaluate the proposed scheme from different aspects and compare it with the state-of-the-art schemes. After a large number of experimental tests, the encouraging results obtained prove the effectiveness of our watermarking scheme.

Color image watermarking based on reflectance component modification and guided image filtering

Digital images available on the Internet can be effortlessly copied and redistributed. Many image watermarking methods have been developed and are used as technical solutions to trace the ownership. Various watermark embedding and extraction techniques were considered and used in order to obtain reliable extraction and robustness against attacks. This paper presents a new color image watermarking method based on modification of the reflectance component in the Hue-Saturation-Value (HSV) color space. In the watermark embedding process, the reflectance component extracted from the S component is modified in accordance with Just Noticeable Difference (JND) thresholds derived from the V component. Guided image filtering is used in watermark extraction to predict the original reflectance component, and blind extraction is achieved by subtracting the predicted component from the watermarked one. The performances of five watermarking methods, including the proposed method, were evaluated and compared for accuracy and robustness at the equivalent quality of watermarked images. The results demonstrate that the proposed method provides an improved quality of extracted watermark by both objective and subjective quality measures. It is also more robust than the previous methods against various types of image processing based attacks, including the Stirmark benchmark.

Blind and safety-enhanced dual watermarking algorithm with chaotic system encryption based on RHFM and DWT-DCT

This study proposes a novel blind watermarking algorithm based on radial harmonic Fourier moments (RHFMs) as well as discrete wavelet transform and discrete cosine transform (DWT-DCT) to achieve copyright protection. A non-adjacent coupled map lattice chaotic system was used to improve the safety in the watermark embedding process. First, a zero-watermark was obtained by performing DCT on the original image. Meanwhile, the direction information of the origin was calculated and embedded in the RHFM domain. During normal watermark embedding, the image-embedded direction information was transformed by a one-level DWT and the low-frequency sub-band was divided into 8×8 non-overlapping blocks. Each block was transformed by DCT, and two low-frequency coefficients were selected to embed the encrypted watermark. Experimental results demonstrated that the proposed algorithm is robust against rotation and other attacks. The analysis also showed that the proposed algorithm satisfies the invisibility of watermarking algorithms and has good security performance.

A new structure-preserving quaternion QR decomposition method for color image blind watermarking

Most of the existing color image watermarking schemes are designed to mark each color channel individually, which ignores the correlation of different color channels and the synchronous embedding of watermarks. In this paper, we propose a robust blind watermarking scheme based on quaternion QR decomposition (QQRD) for color image copyright protection, while using algebraic structure-preserving method to release its computational complexity. In watermark embedding process, QQRD is applied to non-overlapping 4×4 image blocks in a whole manner, whereupon three strong correlation coefficient pairs determined by the Normalized Cross-Correlation (NC) method are modified to embed color watermark within factorized matrices. This avoids layered processing that causes abnormal sensitivity to attacks and less correlation that causes excessive modification of coefficient values. Numerical results show that the newly proposed watermarking scheme has strong ability to resist most image processing attacks and is error-free under no attack, brightness adjustment attack and Poisson noise attack. Its superiority over other state-of-the-art methods is also experimentally demonstrated.

Copyright protection method of big data based on nash equilibrium and constraint optimization

Data watermarking technology is an effective means to protect the copyright of big data. In order to embed robust and highly available data watermarks, firstly, based on the game theory, a Nash equilibrium model between watermark robustness and data quality is established to solve the optimal number of data group. Then, the mapping relationship between data group and watermark bit is established by using secure hash algorithm.Finally, under the constraint of data usability, the improved particle swarm optimization algorithm is used to solve the optimal solution of data change for each data group, and then the data is changed accordingly to complete the embedding of watermark bit. In order to verify the copyright ownership of big data, the corresponding watermark extraction method is also given in this paper. Watermark extraction is the inverse process of watermark embedding. First, it traverses all the groups and extracts the bits that might be embedded in each group. Then, the actual embedded watermark bit is finally determined by most election strategies. The experimental results show that the proposed method can not only detect watermarks under different attack conditions, ensure the robustness of big data watermarks, but also achieve better data quality, and the comprehensive effect of data watermarks is better than the existing methods.

Embedding and Extraction of Color Image Digital Watermark Based on Quaternion Fourier Transform

With the rapid development of computer and network technology, digital products, especially color digital images, can be popularized on the Internet, but because of their easy access and large-scale copying characteristics, their copyright protection has become increasingly difficult, and piracy and infringement and other issues are becoming more and more serious. Therefore, watermarking technology has increasingly attracted people's attention. Whether it is using digital or color images as host graphics or watermarking materials, it has become one of the hottest watermarking technologies. This article intends to explore the method of embedding and extracting the color image digital watermark using quaternion Fourier transform, and theoretically analyzes the quaternion representation, quaternion Fourier transformation, and logarithmic polar coordinate transformation of color images. And select the middle and low frequency area of the final real part of the quaternion Fourier transform as the embedding area, and carry out the method and process of watermark embedding and extraction. The simulation results show that, in the most typical attack form, the above-mentioned dynamic watermark embedding and extraction algorithm can not only effectively combat filtering attacks and noise intrusion, but also has absolute advantages in combating shear damage.

Antitamper Image Watermarking Based on Cellular Network Topology for IoT‐Themed Mobile Forensics

The Internet of Things (IoT) connects physical and digital worlds with mobile devices, accompanied by a surge in cybersecurity issues. With the rapid adoption of mobile devices, mobile forensics emerges as a new interdisciplinary field that concerns many forms of sabotage and cybercrime in the context of mobile computing. One of the most common cyberattacks is tampering. Digital watermarking is a tamper‐evident technique used to protect data integrity. In this paper, we present an antitamper image watermarking scheme designed for mobile communications with low computational cost. A reference matrix based on cellular network topology is introduced to guide the watermark embedding and extraction processes. This reference matrix serves as a lookup table to reduce computational complexity, thereby enabling efficient implementation on mobile devices. Our scheme is aimed at offering high accuracy in detecting and localizing tampered regions. We also achieve a high watermarking capacity while leaving the visual quality of the carrier images nearly unharmed. Experimental results validate the effectiveness of our scheme against various types of simulated forgery including cropping and copy/paste attacks.

A novel Collatz conjecture-based digital image watermarking method

In this research, a novel Collatz conjecture-based image transformation and a modified logistic map are used for digital image watermarking. The proposed modified logistic map is utilized as a pseudo-random number generator (PRNG) to select the pixel to be watermarked. The host image is divided into nonoverlapping blocks, for instance, 2 × 2, 3 × 3, 4 × 4, 8 × 8, and 10 × 10 size blocks. A Collatz transformation is used on the selected pixel to generate a Collatz value because watermark embedding and extraction processes are implemented on the Collatz values. Evaluation criteria, such as visual quality, payload capacity, robustness, and execution time have been used to measure the performance of the proposed method. The results obtained and calculated clearly show that the proposed Collatz-based watermarking method has high visual quality, high payload capacity, and shorter execution time. Also, the proposed method has a basic mathematical structure and it can be programmed basically. This situation shows that the proposed method can be used in real-time applications. Collatz values can be changed with minimal distortions; therefore, this method is not robust against attacks. Finally, a successful image watermarking method is presented in this method using Collatz conjecture.

Watermarking techniques for medical data authentication: a survey

With the widespread growth of medical images and improved communication and computer technologies in recent years, authenticity of the images has been a serious issue for E-health applications. In order to this, various notable watermarking techniques are developed by potential researchers. However, those techniques are unable to solve many issues that are necessary to be measured in future investigations. This paper surveys various watermarking techniques in medical domain. Along with the survey, general concepts of watermarking, major characteristics, recent applications, concepts of embedding and recovery process of watermark, and the summary of various techniques (in tabular form) are highlighted in brief. Further, major issues associated with medical image watermarking are also discussed to find out research directions for fledgling researchers and developers.

A Blind Watermarking Algorithm for Digital Image Based on DWT

In order to improve the robustness of digital image watermarking algorithms, the digital image blind watermarking algorithm proposed in this paper embeds watermark information in a reasonable position. First, scramble the watermark information and embed the random signal into the low-frequency domain of the image. On the one hand, it can effectively prevent attacks on the watermark system and enhance the security of the system, disperse as much as possible. In the watermark embedding process, a method based on the wavelet transform domain is used. The original image is not required to participate in the watermark extraction process, and the blind watermark function is implemented. In order to verify the effectiveness of the algorithm, the watermarked image is subjected to filtering, scaling, noise, cropping and rotation attacks, and the peak signal-to-noise ratio and normalized correlation coefficient are used to quantitatively evaluate the watermarked image. Experimental results show that the algorithm can resist a variety of attacks, and has good imperceptibility and robustness.

Optimization insisted watermarking model: hybrid firefly and Jaya algorithm for video copyright protection

In this digital era, illegitimate redistribution and protection of digital multimedia have to turn out to be the critical issue. Therefore, digital watermarking has been introduced for avoiding illegitimate works and also to ensure security and authentication as well. “Digital video watermarking is a method to hide some kind of data like audio, image, text into digital video sequences which is nothing but orders of successive still images.” This paper intends to formulate a novel video watermarking framework that includes three stages, such as (i) optimal video frame prediction, (ii) watermark embedding process and (iii) watermark extraction process. Very first, the optimal frames are determined using a new hybrid algorithm termed trial-based update on Jaya plus firefly (TU-JF) algorithm, in such a way that the peak signal-to-noise ratio (PSNR) should be maximal. The frames are assigned with a label of one or zero, where label one denotes the frame with better PSNR (can select for embedding process) and label zero denotes the frame with reduced PSNR (cannot be used for embedding). Consequently, a data library is formed from the obtained results, where each frame of videos is determined with their gray-level co-occurrence matrix (GLCM) features and labels (can embed or not). As in the proposed model, the optimal frame prediction is carried out using deep belief network (DBN) framework; the obtained data are then trained in the model. The optimal frames could be predicted in an efficient manner while testing. The significant contribution of this work concerns the optimization of hidden neurons in the DBN framework, which helps to enhance prediction accuracy. At last, the “watermark embedding process” and “watermark extraction process” are done by which the image could be embedded within the optimal frames.