Showing papers on "Watermark published in 2019"

DeepSigns: An End-to-End Watermarking Framework for Ownership Protection of Deep Neural Networks

Abstract: Deep Learning (DL) models have created a paradigm shift in our ability to comprehend raw data in various important fields, ranging from intelligence warfare and healthcare to autonomous transportation and automated manufacturing. A practical concern, in the rush to adopt DL models as a service, is protecting the models against Intellectual Property (IP) infringement. DL models are commonly built by allocating substantial computational resources that process vast amounts of proprietary training data. The resulting models are therefore considered to be an IP of the model builder and need to be protected to preserve the owner's competitive advantage. We propose DeepSigns, the first end-to-end IP protection framework that enables developers to systematically insert digital watermarks in the target DL model before distributing the model. DeepSigns is encapsulated as a high-level wrapper that can be leveraged within common deep learning frameworks including TensorFlow and PyTorch. The libraries in DeepSigns work by dynamically learning the Probability Density Function (pdf) of activation maps obtained in different layers of a DL model. DeepSigns uses the low probabilistic regions within the model to gradually embed the owner's signature (watermark) during DL training while minimally affecting the overall accuracy and training overhead. DeepSigns can demonstrably withstand various removal and transformation attacks, including model pruning, model fine-tuning, and watermark overwriting. We evaluate DeepSigns performance on a wide variety of DL architectures including wide residual convolution neural networks, multi-layer perceptrons, and long short-term memory models. Our extensive evaluations corroborate DeepSigns' effectiveness and applicability. We further provide a highly-optimized accompanying API to facilitate training watermarked neural networks with a training overhead as low as 2.2%.

Survey of robust and imperceptible watermarking

Abstract: Robustness, imperceptibility and embedding capacity are the preliminary requirements of any watermarking technique. However, research concluded that these requirements are difficult to achieve at same time. In this paper, we review various recent robust and imperceptible watermarking methods in spatial and transform domain. Further, the paper introduces elementary concepts of digital watermarking, characteristics and novel applications of watermark in detail. Furthermore, various analysis and comparison of different notable watermarking techniques are discussed in tabular format. We believe that our survey contribution will helpful for fledgling researchers to develop robust and imperceptible watermarking algorithms for various practical applications.

A novel hybrid DCT and DWT based robust watermarking algorithm for color images

Abstract: In this paper, a novel robust color image watermarking method based on Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) is proposed. In this method, RGB cover image is divided into red, green and blue components. DCT and DWT are applied to each color components. Grayscale watermark image is scrambled by using Arnold transform. DCT is performed to the scrambled watermark image. Transformed watermark image is then divided into equal smaller parts. DCT coefficients of each watermark parts are embedded into four DWT bands of the color components of the cover image. The robustness of the proposed color image watermarking has been demonstrated by applying various image processing operations such as rotating, resizing, filtering, jpeg compression, and noise adding to the watermarked images. Experimental results show that the proposed method is robust to the linear and nonlinear attacks and the transparency of the watermarked images has been protected.

How to prove your model belongs to you: a blind-watermark based framework to protect intellectual property of DNN

Abstract: Deep learning techniques have made tremendous progress in a variety of challenging tasks, such as image recognition and machine translation, during the past decade. Training deep neural networks is computationally expensive and requires both human and intellectual resources. Therefore, it is necessary to protect the intellectual property of the model and externally verify the ownership of the model. However, previous studies either fail to defend against the evasion attack or have not explicitly dealt with fraudulent claims of ownership by adversaries. Furthermore, they can not establish a clear association between the model and the creator's identity. To fill these gaps, in this paper, we propose a novel intellectual property protection (IPP) framework based on blind-watermark for watermarking deep neural networks that meet the requirements of security and feasibility. Our framework accepts ordinary samples and the exclusive logo as inputs, outputting newly generated samples as watermarks, which are almost indistinguishable from the origin, and infuses these watermarks into DNN models by assigning specific labels, leaving the backdoor as the basis for our copyright claim. We evaluated our IPP framework on two benchmark datasets and 15 popular deep learning models. The results show that our framework successfully verifies the ownership of all the models without a noticeable impact on their primary task. Most importantly, we are the first to successfully design and implement a blind-watermark based framework, which can achieve state-of-art performances on undetectability against evasion attack and un-forgeability against fraudulent claims of ownership. Further, our framework shows remarkable robustness and establishes a clear association between the model and the author's identity.

An Optimized Image Watermarking Method Based on HD and SVD in DWT Domain

Abstract: In this paper, a novel image watermarking method is proposed which is based on discrete wave transformation (DWT), Hessenberg decomposition (HD), and singular value decomposition (SVD). First, in the embedding process, the host image is decomposed into a number of sub-bands through multi-level DWT, and the resulting coefficients of which are then used as the input for HD. The watermark is operated on the SVD at the same time. The watermark is finally embedded into the host image by the scaling factor. Fruit fly optimization algorithm, one of the natural-inspired optimization algorithms is devoted to find the scaling factor through the proposed objective evaluation function. The proposed method is compared to other research works under various spoof attacks, such as the filter, noise, JPEG compression, JPEG2000 compression, and sharpening attacks. The experimental results show that the proposed image watermarking method has a good trade-off between robustness and invisibility even for the watermarks with multiple sizes.

Screen-Shooting Resilient Watermarking

Abstract: This paper proposes a novel screen-shooting resilient watermarking scheme, which means that if the watermarked image is displayed on the screen and the screen information is captured by the camera, we can still extract the watermark message from the captured photo. To realize such demands, we analyzed the special distortions caused by the screen-shooting process, including lens distortion, light source distortion, and moire distortion. To resist the geometric deformation caused by lens distortion, we proposed an intensity-based scale-invariant feature transform (I-SIFT) algorithm which can accurately locate the embedding regions. As for the loss of image details caused by light source distortion and moire distortion, we put forward a small-size template algorithm to repeatedly embed the watermark into different regions, so that at least one complete information region can survive from distortions. At the extraction side, we designed a cross-validation-based extraction algorithm to cope with repeated embedding. The validity and correctness of the extraction method are verified by hypothesis testing. Furthermore, to boost the extraction speed, we proposed a SIFT feature editing algorithm to enhance the intensity of the keypoints, based on which, the extraction accuracy and extraction speed can be greatly improved. The experimental results show that the proposed watermarking scheme achieves high robustness for screen-shooting process. Compared with the previous schemes, our algorithm provides significant improvement in robustness for screen-shooting process and extraction efficiency.

Robust and distortion control dual watermarking in LWT domain using DCT and error correction code for color medical image

Abstract: This paper presents lifting wavelet transform (LWT) and discrete cosine transform (DCT) based robust watermarking approach for tele-health applications. For identity authentication, ‘signature watermark’ of size ‘64 × 64’ and ‘patient report’ of size ‘80’ characters are hiding into the host medical image. Further, the signature watermark is encrypted by message-digest (MD5) and ‘patient report’ is encoded by BCH error correcting code before embedding into the host image. Experimental demonstrations indicate that the method provides sufficient robustness and security against various attacks without significant distortion between cover and watermarked image. Further, our results proved that the method offer NC value more than 0.9214 for most of the considered attacks. Furthermore, it is evident from results our method shows the improvement in robustness to previously reported techniques under consideration while providing low computational complexity.

Robust Watermarking of Neural Network with Exponential Weighting

Abstract: Deep learning has been achieving top levels of performance in many tasks. However, since it is costly to train a deep learning model, neural network models must be treated as valuable intellectual properties. One concern arising from our current situation is that malicious users might redistribute proprietary models or provide prediction services using such models without permission. One promising solution to this problem is digital watermarking, which works by embedding a mechanism into the model so that the model owners can verify their ownership of the model externally. In this study, we present a novel attack method against such watermarks known as query modification and demonstrate that all currently existing watermarking methods are vulnerable to either query modification or other existing attack methods (such as model modification). To overcome these vulnerabilities, we then present a novel watermarking method that we have named exponential weighting and experimentally show that our watermarking method achieves high watermark verification performance even under malicious invalidation processing attempts by unauthorized service providers (such as model modification and query modification) without sacrificing the predictive performance of the neural network model itself.

A novel robust reversible watermarking scheme for protecting authenticity and integrity of medical images

Abstract: It is of great importance in telemedicine to protect authenticity and integrity of medical images. They are mainly addressed by two technologies, which are region of interest (ROI) lossless watermarking and reversible watermarking. However, the former causes biases on diagnosis by distorting region of none interest (RONI) and introduces security risks by segmenting image spatially for watermark embedding. The latter fails to provide reliable recovery function for the tampered areas when protecting image integrity. To address these issues, a novel robust reversible watermarking scheme is proposed in this paper. In our scheme, a reversible watermarking method is designed based on recursive dither modulation (RDM) to avoid biases on diagnosis. In addition, RDM is combined with Slantlet transform and singular value decomposition to provide a reliable solution for protecting image authenticity. Moreover, ROI and RONI are divided for watermark generation to design an effective recovery function under limited embedding capacity. Finally, watermarks are embedded into whole medical images to avoid the risks caused by segmenting image spatially. Experimental results demonstrate that our proposed lossless scheme not only has remarkable imperceptibility and sufficient robustness, but also provides reliable authentication, tamper detection, localization and recovery functions, which outperforms existing schemes for protecting medical images

A new DCT-based robust image watermarking method using teaching-learning-Based optimization

Abstract: Despite the passage of more than 20 years of raising the issue of watermarking by Tirkel, the researchers are still seeking to provide an approach more resistant than existing solutions. In this paper we proposed a new watermarking method that works on JPEG-YCbCr color space and the embedding operation is based on the relationships between the DCT coefficients. The JPEG-YCbCr is rescaling of YCbCr color space that has a good robustness against most of the attacks and it used in JPEG image format. Also the relationship between the DCT coefficients is stable against most of the changes in the host image. Therefore, the proposed method has more robustness compared to some other methods. On the other hand, many intelligent optimization methods are in use regarding the nature of the phenomenon simulated by the methods. Teaching-Learning-Based Optimization (TLBO) is a novel method of optimization which has become a hot issue in recent years. The algorithm works on the principle of teaching and learning, where teachers increase the knowledge of students and also the students learn from interaction among themselves. The proposed method uses TLBO which has been applied rarely so far in watermarking algorithms and it can automatically determine the embedding parameters and suitable position for inserting the watermark. Besides, in the object function of TLBO, ensuring higher imperceptibility and also robustness against Median filter and JPEG compression have been considered. According to the experimental results, the imperceptibility of watermarked images is satisfactory, and embedded watermark is extracted successfully even if the watermarked image is distorted by various attacks.

A Robust Watermarking Scheme in YCbCr Color Space Based on Channel Coding

Abstract: The rapid development of big data and cloud computing technologies greatly accelerate the spreading and utilization of images and videos. The copyright protection for images and videos is becoming increasingly serious. In this paper, we proposed the robust non-blind watermarking schemes in YCbCr color space based on channel coding. The source watermark image is encoded and singular value decomposed. Subsequently, the singular value matrixes are embedded into the Y, Cb, and Cr components of the host image after four-level discrete wavelet transform (DWT). The embedding factor for each component is calculated based on the just-noticeable distortion and the singular vectors of HL subband of DWT. The peak signal-to-noise ratio of the watermarked image and the normalized correlation coefficient of the extracted watermark are investigated. It is shown that the proposed channel coding-based schemes can achieve near exact watermark recovery against all kinds of attacks. Considering both robustness and transparency, the convolutional code-based additive embedding scheme is optimal, which can also achieve good performance for video watermarking after extension.

Secure and robust watermark scheme based on multiple transforms and particle swarm optimization algorithm

Abstract: To improve the security, robustness and imperceptibility of watermark schemes, a novel watermark scheme is devised by fusing multiple watermark techniques, including lifting wavelet transform, discrete cosine transform, discrete fractional angular transform and singular value decomposition. To solve the false positive problem in various SVD-based watermark schemes, transform domain encryption is utilized and the embedding component of watermark instead of the whole watermark is embedded into the host image. Furthermore, the particle swarm optimization algorithm is used to optimize the scaling factors and the parameter of the improved discrete fractional angular transform. The proposed watermark scheme is tested by several attacks, such as JPEG compression, low-pass filtering, Histogram equalization, contrast enhance, and etc. Simulation results demonstrate that the proposed watermark scheme is superior in the aspects of security, robustness and imperceptibility.

Attacks on Digital Watermarks for Deep Neural Networks

Abstract: Training deep neural networks is a computationally expensive task. Furthermore, models are often derived from proprietary datasets that have been carefully prepared and labelled. Hence, creators of deep learning models want to protect their models against intellectual property theft. However, this is not always possible, since the model may, e.g., be embedded in a mobile app for fast response times. As a countermeasure watermarks for deep neural networks have been developed that embed secret information into the model. This information can later be retrieved by the creator to prove ownership. Uchida et al. proposed the first such watermarking method. The advantage of their scheme is that it does not compromise the accuracy of the model prediction. However, in this paper we show that their technique modifies the statistical distribution of the model. Using this modification we can not only detect the presence of a watermark, but even derive its embedding length and use this information to remove the watermark by overwriting it. We show analytically that our detection algorithm follows consequentially from their embedding algorithm and propose a possible countermeasure. Our findings shall help to refine the definition of undetectability of watermarks for deep neural networks.

A Robust Multi-Watermarking Algorithm for Medical Images Based on DTCWT-DCT and Henon Map

Abstract: To resolve the contradiction between existing watermarking methods—which are not compatible with the watermark’s ability to resist geometric attacks—and robustness, a robust multi-watermarking algorithm suitable for medical images is proposed. First, the visual feature vector of the medical image was obtained by dual-tree complex wavelet transform and discrete cosine transform (DTCWT-DCT) to perform multi-watermark embedding and extraction. Then, the multi-watermark was pre-processed using the Henon map chaotic encryption technology to strengthen the security of watermark information, and combined with the concept of zero watermark to make the watermark able to resist both conventional and geometric attacks. Experimental results show that the proposed algorithm can effectively extract watermark information; it implements zero watermarking and blind extraction. Compared with existing watermark technology, it has good performance in terms of its robustness and resistance to geometric attacks and conventional attacks, especially in geometric attacks.

REFIT: a Unified Watermark Removal Framework for Deep Learning Systems with Limited Data

Abstract: Training deep neural networks from scratch could be computationally expensive and requires a lot of training data. Recent work has explored different watermarking techniques to protect the pre-trained deep neural networks from potential copyright infringements. However, these techniques could be vulnerable to watermark removal attacks. In this work, we propose REFIT, a unified watermark removal framework based on fine-tuning, which does not rely on the knowledge of the watermarks, and is effective against a wide range of watermarking schemes. In particular, we conduct a comprehensive study of a realistic attack scenario where the adversary has limited training data, which has not been emphasized in prior work on attacks against watermarking schemes. To effectively remove the watermarks without compromising the model functionality under this weak threat model, we propose two techniques that are incorporated into our fine-tuning framework: (1) an adaption of the elastic weight consolidation (EWC) algorithm, which is originally proposed for mitigating the catastrophic forgetting phenomenon; and (2) unlabeled data augmentation (AU), where we leverage auxiliary unlabeled data from other sources. Our extensive evaluation shows the effectiveness of REFIT against diverse watermark embedding schemes. In particular, both EWC and AU significantly decrease the amount of labeled training data needed for effective watermark removal, and the unlabeled data samples used for AU do not necessarily need to be drawn from the same distribution as the benign data for model evaluation. The experimental results demonstrate that our fine-tuning based watermark removal attacks could pose real threats to the copyright of pre-trained models, and thus highlight the importance of further investigating the watermarking problem and proposing more robust watermark embedding schemes against the attacks.

On the Robustness of the Backdoor-based Watermarking in Deep Neural Networks

Abstract: Obtaining the state of the art performance of deep learning models imposes a high cost to model generators, due to the tedious data preparation and the substantial processing requirements. To protect the model from unauthorized re-distribution, watermarking approaches have been introduced in the past couple of years. We investigate the robustness and reliability of state-of-the-art deep neural network watermarking schemes. We focus on backdoor-based watermarking and propose two -- a black-box and a white-box -- attacks that remove the watermark. Our black-box attack steals the model and removes the watermark with minimum requirements; it just relies on public unlabeled data and a black-box access to the classification label. It does not need classification confidences or access to the model's sensitive information such as the training data set, the trigger set or the model parameters. The white-box attack, proposes an efficient watermark removal when the parameters of the marked model are available; our white-box attack does not require access to the labeled data or the trigger set and improves the runtime of the black-box attack up to seventeen times. We as well prove the security inadequacy of the backdoor-based watermarking in keeping the watermark undetectable by proposing an attack that detects whether a model contains a watermark. Our attacks show that a recipient of a marked model can remove a backdoor-based watermark with significantly less effort than training a new model and some other techniques are needed to protect against re-distribution by a motivated attacker.

New Rapid and Robust Color Image Watermarking Technique in Spatial Domain

Abstract: In this paper, a novel spatial domain color image watermarking technique is proposed to rapidly and effectively protect the copyright of the color image. First, the direct current (DC) coefficient of 2D-DFT obtained in the spatial domain is discussed, and the relationship between the change of each pixel in the spatial domain and the change of the DC coefficient in the Fourier transform is proved. Then, the DC coefficient is used to embed and extract watermark in the spatial domain by the proposed quantization technique. The novelties of this paper include three points: 1) the DC coefficient of 2D-DFT is obtained in the spatial domain without of the true 2D-DFT; 2) the relationship between the change of each pixel in the image block and the change of the DC coefficient of 2D-DFT is found, and; 3) the proposed method has the short running time and strong robustness. The experimental results on two publicly available image databases (CVG-UGR and USC-SIPI) have shown that the proposed method not only has satisfied the needs of invisibility but also has better performance in terms of robustness and real-time feature, which show the proposed method has both advantages of spatial domain and frequency domain.

Quantization based multiple medical information watermarking for secure e-health

Abstract: In this paper, an improved wavelet based medical image watermarking algorithm is proposed. Initially, the proposed technique decomposes the cover medical image into ROI and NROI regions and embedding three different watermarks into the non-region of interest (NROI) part of the transformed DWT cover image for compact and secure medical data transmission in E-health environment. In addition, the method addressing the problem of channel noise distortion may lead to faulty watermark by applying error correcting codes (ECCs) before embedding them into the cover image. Further, the bit error rates (BER) performance of the proposed method is determined for different kind of attacks including ‘Checkmark’ attacks. Experimental results indicate that the Turbo code performs better than BCH (Bose-Chaudhuri-Hochquenghem) error correction code. Furthermore, the experimental results validate the effectiveness of the proposed framework in terms of BER and embedding capacity compared to other state-of-the-art methods. Therefore, the proposed method finds potential application in prevention of patient identity theft in e-health applications.

Hybrid secure and robust image watermarking scheme based on SVD and sharp frequency localized contourlet transform

Abstract: In this paper, using singular value decomposition (SVD) and sharp frequency localized contourlet transform (SFLCT) a secure and robust image watermarking procedure is introduced. The SVD and SFLCT are applied on both watermark and original images and using the properties of the SVD and utilizing the advantages of the SFLCT, noticeable results of the watermarking requirements are obtained. Since most of the SVD based watermarking schemes are not resistant against ambiguity attacks and suffer from the false positive problem, this objection is resolved without adding extra steps to the watermarking algorithm and the suggested scheme is secure and resistant against ambiguity attacks. The simulation of the scheme is implemented and its robustness against various types of attacks is experimented. In comparison with some of the recent schemes this procedure shows high imperceptibility, capacity and robustness and these features make the scheme a suitable choice for the image processing applications.

A novel hash function based fragile watermarking method for image integrity

Abstract: In recent years, tampering and altering of digital images have become easier with the rapid development of computer technologies such as digital image editing tools. Therefore, verification of image integrity and tamper detection of digital images have become a great challenge. Fragile watermarking is the most widely used method for protecting the integrity and content authenticity of the image. In this paper, by using SHA-256 hash function, a novel block based fragile watermark embedding and tamper detection method is proposed. In watermark embedding phase, host image is divided into 32 × 32 non-overlapped blocks. Each 32 × 32 block is then divided into four 16 × 16 nonoverlapped sub-blocks. The entire hash value of the first three sub-blocks is generated as a watermark using SHA-256 hash function. The generated 256-bit binary watermark is embedded into the least significant bits (LSBs) of the fourth sub-block and watermarked image is obtained. In tamper detection phase, the detection of tampered block has been performed by comparing the hash value obtained from the three sub-blocks with the extracted watermark from the fourth sub-block of the watermarked image. The performance of the proposed method has been evaluated by applying linear and nonlinear attacks to the different regions of the watermarked images. Experimental results show that the proposed method detects all the tampered regions of the attacked images and high visual quality of watermarked images has been obtained.

Enhancing Image Watermarking With Adaptive Embedding Parameter and PSNR Guarantee

Abstract: Watermarking plays an important role in identifying the copyright of an image and related issues. The state-of-the-art watermark embedding schemes, spread spectrum and quantization, suffer from host signal interference (HSI) and scaling attacks, respectively. Both of them use a fixed embedding parameter, which is difficult to take both robustness and imperceptibility into account for all images. This paper solves the problems by proposing two novel blind watermarking schemes: a spread spectrum scheme with adaptive embedding strength (SSAES) and a differential quantization scheme with adaptive quantization threshold (DQAQT). Their adaptiveness comes from the proposed adaptive embedding strategy (AEP), which maximizes the embedding strength or quantization threshold by guaranteeing the peak signal-to-noise ratio (PSNR) of the host image after embedding the watermark, and strikes the balance between robustness and imperceptibility. SSAES is HSI free by factoring in the priori knowledge about HSI. In DQAQT, an effective quantization mode is proposed to resist scaling attacks by utilizing the difference between two selected DCT coefficients with high stability. Both SSAES and DQAQT can be easily applied to other watermarking frameworks. We introduce a notion called error threshold to theoretically analyze the performance of our proposed methods in details. The experimental results consistently demonstrate that SSAES and DQAQT outperform the state-of-the-art methods in terms of imperceptibility, robustness, computational cost, and adaptability.

Biometric-based efficient medical image watermarking in E-healthcare application

Abstract: Information hiding is particularly used for security applications to protect the secret message from an unauthorised person. Due to the tremendous development of the Internet and its usage, the issue of protection over the internet is increasing. Under such a condition, transforming the information from the transmitter to the receiver requires more security. Accordingly, in my previous research, an efficient medical image watermarking technique in E-healthcare application using a combination of compression and cryptography algorithm was proposed. The system only gives confidentiality and reliability. To overcome the problem, the authors propose a biometric-based on an efficient medical image watermarking in E-healthcare application, which produces a system for authentication, confidentiality, and reliability of the system. The proposed system utilises the fingerprint biometric for authentication, cryptography process for confidentiality, and reversible watermarking for the integrity. Basically, the proposed system consists of two stages such as (i) watermark embedding process and (ii) watermark extraction process. The experiments were carried out on the different medical images with electronic health record and the effectiveness of the proposed algorithm is analysed with the help of peak signal-to-noise ratio and normalised correlation.

An Approximate Schur Decomposition-Based Spatial Domain Color Image Watermarking Method

Abstract: In this paper, an approximate Schur decomposition-based spatial domain blind color image watermarking method is proposed to protect the copyright of color images, which has low computation complexity similiar to the watermarking technique in the spatial domain and strong robustness similiar to the watermarking technique in the transform domain. First, the approximate maximum eigenvalue of Schur decomposition is calculated in the spatial domain by the proposed method. Second, the approximate maximum eigenvalue is used to embed and extract the color watermark image in the spatial domain without the true Schur decomposition. Moreover, the procedures of the proposed watermarking method are given in detail. The proposed technique is performed on the spatial domain based on the approximate Schur decomposition and belongs to blind watermarking technique. The experimental results on two publicly available image databases (CVG-UGR and USC-SIPI) have demonstrated the effectiveness of the proposed method in terms of invisibility, robustness, and the real-time feature.

A New Robust Approach for Reversible Database Watermarking with Distortion Control

Abstract: Nowadays information is crucial in many fields such as medicine, science and business, where databases are used effectively for information sharing. However, the databases face the risk of being pirated, stolen or misused, which may result in a lot of security threats concerning ownership rights, data tampering and privacy protection. Watermarking is utilized to enforce ownership rights on shared relational databases. Many reversible watermarking methods are proposed recently to protect rights of owners along with recovering original data. Most state-of-the-art methods modify the original data to a large extent, result in data quality degradation, and cannot achieve good balance between robustness against malicious attacks and data recovery. In this paper, we propose a robust and reversible database watermarking technique, Genetic Algorithm and Histogram Shifting Watermarking (GAHSW), for numerical relational database. The genetic algorithm is used to select the best secret key for grouping database, where the watermarking can be embedded with balanced distortion and capacity. The histogram of the prediction error is shifted to embed the watermark with good robustness. Experimental results demonstrate the effectiveness of GAHSW and show that it outperforms state-of-the-art approaches in terms of robustness against malicious attacks and preservation of data quality.