Zero-watermarking Algorithm Research Articles

Multi-Instance Zero-Watermarking Algorithm for Vector Geographic Data

To address the variability and complexity of attack types, this paper proposes a multi-instance zero-watermarking algorithm that goes beyond the conventional one-to-one watermarking approach. Inspired by the class-instance paradigm in object-oriented programming, this algorithm constructs multiple zero watermarks from a single vector geographic dataset to enhance resilience against diverse attacks. Normalization is applied to eliminate dimensional and deformation inconsistencies, ensuring robustness against non-uniform scaling attacks. Feature triangle construction and angle selection are further utilized to provide resistance to interpolation and compression attacks. Moreover, angular features confer robustness against translation, uniform scaling, and rotation attacks. Experimental results demonstrate the superior robustness of the proposed algorithm, with normalized correlation values consistently maintaining 1.00 across various attack scenarios. Compared with existing methods, the algorithm exhibits superior comprehensive robustness, effectively safeguarding the copyright of vector geographic data.

Medical Image Zero Watermarking Algorithm based on dual-tree complex wavelet transform, AlexNet and discrete cosine transform

This paper proposed for medical image using zero watermarking, dual-tree complex wavelet transform (DTCWT)-AlexNet and discrete cosine transform (DCT). Furthermore, we utilize the pre-training network AlexNet to extract significant characteristics from medical images. In addition, DTCWT and DCT are employed to convert the complex features, while a perceptual hash function is utilized to cause the feature vector. The original watermark is chaotically scrambled, an exclusive-OR (XOR) gate is inserted into the medical image, and a logical key vector is generated and stored to encrypt the watermark. In general, the medical image under test is used to cutting the important features and create a feature vector using the feature extraction technique. Subsequently, the logical key vector and the feature vector undergo an XOR operation to produce the encrypted watermark. By calculating the normalized correlation (NC) coefficient, the recovered watermark can be utilized to regulate the rights and specifics of the watermark in the medical image after obtaining and decrypting the encrypted watermark with greater than 0.5 NC values. The algorithm's design method tackles the challenge of protecting watermarks from conventional (Gaussian noise, JPEG, median filter) and geometric attacks ( rotation clockwise, anticlockwise, scaling, translation left, translation right, clipping X and Y direction) by incorporating the principles of DTCWT, AlexNet, DCT, and zero watermarking. Encrypting the watermark image through a scrambling process enhances the security of the approach, by ensuring that the watermark should not affect the quality of the image in any way. The research demonstrates that the proposed algorithm is robust and secure for protecting sensitive health information.

A Zero-Watermarking Algorithm Based on Vortex-like Texture Feature Descriptors

For effective copyright protection of digital images, this paper proposes a zero-watermarking algorithm based on local image feature information. The feature matrix of the algorithm is derived from the keypoint set determined by the Speeded-Up Robust Features (SURF) algorithm, and it calculates both the gradient feature descriptors and the vortex-like texture feature (VTF) descriptors of the keypoint set. Unlike traditional texture feature descriptors, the vortex-like texture feature descriptors proposed in this paper contain richer information and exhibit better stability. The advantage of this algorithm lies in its ability to calculate the keypoints of the digital image and provide a stable vector description of the local features of these keypoints, thereby reducing the amount of erroneous information introduced during attacks. Analysis of experimental data shows that the algorithm has good effectiveness, distinguishability, security, and robustness.

Using Zero-Watermarking Algorithm to Protect Arabic Text Documents

In today's digital age where powerful communication media such as the Internet allow for efficient information exchange, copyright protection and authentication of digital content has become an important issue. Plain text is the most widely used means of exchanging information online, and it is important to verify the authenticity of the text. Very limited techniques are available for watermarking and authentication of Arabic text. This paper aims to develop a zero-watermark algorithm to protect the Arabic digital content from being tampering. The algorithm creates an accurate zero watermark based on the characteristics of the content of the Arabic text itself. The watermark is determined by the frequency of appearance of prepositions and their specific locations (في، عن، من، على) in the text. The extraction algorithm used for extracting zero watermark and compared with the created one to prove the authenticity of the Arabic text. This method ensures that any change to the text will lead to a change in the extracted zero watermark, which will facilitate the detection process upon verification. Experimental results demonstrate the effectiveness of the algorithm in discovering text tampering (addition, deletion, and rephrasing), tested on nine different text samples with various lengths and attacks, the proposed approach is robust in detecting modifications to the text, even if they are minor, compared to the existing approach.

Resnet50 and logistic Gaussian map-based zero-watermarking algorithm for medical color images

Medical image copyright protection is becoming increasingly relevant as medical images are used more frequently in medical networks and institutions. The traditional embedded watermarking system is inappropriate for medical images since it degrades the original images’ quality. Furthermore, medical-colored image watermarking options are constrained since most medical watermarking systems are built for gray-scale images. This paper proposes a zero-watermarking scheme for medical color image copyright protection based on a chaotic system and Resnet50, which is a convolutional neural network method. The network Resnet50 is used to extract features from the color medical image, and then a logistic Gaussian map is used to scramble these features and scramble the binary image. Finally, an exclusive OR operation is performed (scrambled binary image, scrambled features for the medical color image) to form a zero watermarking. The experimental result proves that our scheme is effective and robust to geometric and common image processing attacks. The BER values of the extracted watermarks are below 0.0039, and the NCC values are above 0.9942, while the average PSNR values of the attacked images are 29.0056 dB. Also, it is superior to other zero-watermark schemes for medical images in terms of robustness to conventional image processing and geometric attacks. Furthermore, the experimental results show that the Resnet50 model outperforms other models in terms of reducing the mean squared errors of the features between the attacked and original image.

INVARIANT ZERO-WATERMARKING ALGORITHM IN DWT-DCT DOMAIN USING ROBUST FEATURES MATCHING

In order to protect the copyright of the digital contents, the robust watermarking techniques are employed based on frequency domains or combined frequency domains. Another techniques, called zero-watermarking which does not modify the original image to embed the watermark but create a watermark from its robust features, is a useful technique for resolving the tension between robustness and invisibility. In this paper, we propose a new zero-watermarking algorithm based on discrete wavelet transform and discrete cosine transform domain with significant feature points matching for improving the robustness. In our proposed method, the original image is firstly separated into three components (Y, Cr, Cb), then its Y-component is performed with discrete wavelet transform, afterwards its LL3 is transformed with discrete cosine transform. To generate the master share of the original image, the DCT-based image is binarized. After performing the Arnold transformation on the watermark, the owner share is generated by taking the XOR operation on the scrambled watermark and master share. The robustness of the our proposed algorithm for imaging processes is analyzed, and the results show that the proposed algorithm is robust to common signal processing such as noise, filtering, JPEG compression, geometric attacks such as rotation, scaling, translation and so on.

Robust zero-watermarking algorithm for multi-medical images based on FFST-Schur and Tent mapping

Aiming at issues such as the difficulty of centralized protection of multiple medical images and the high storage cost caused by repetitive operations, we proposed a robust zero-watermarking algorithm for multiple medical images based on fast finite Shearlet transform (FFST)-Schur and Tent mapping. First, multiple medical images were normalized, and the processed images were fused using a gray-weighted average fusion method for image fusion. Second, an FFST is applied to the fused image to divide the low-frequency sub-bands into blocks of the same size and perform Schur decomposition, which generates a feature image using the magnitude between the largest eigenvalue of each block and the overall average value. Finally, an image encryption algorithm based on Tent mapping is proposed to encrypt the logo image, and the encrypted image and feature image are then subjected to an exclusive OR operation to produce a zero-watermarking image. Experimental results demonstrate that the proposed approach effectively safeguards against standard image processing and geometric and combinatorial attacks. The average performance of the anti-attack algorithm was improved by approximately 3.2 % compared to similar algorithms, indicating the superior robustness of the proposed algorithm.

Retracted: Robust Zero Watermarking Algorithm for Medical Images Based on Zernike-DCT

Retracted: Robust Zero Watermarking Algorithm for Medical Images Based on Zernike-DCT

Retracted: Robust and Secure Zero-Watermarking Algorithm for Medical Images Based on Harris-SURF-DCT and Chaotic Map

Retracted: Robust and Secure Zero-Watermarking Algorithm for Medical Images Based on Harris-SURF-DCT and Chaotic Map

Zero watermarking algorithm for BIM data based on distance partitioning and local feature

ABSTRACT Zero watermarking constructs the watermark information according to the characteristics of the original data, without changing the data structure and data accuracy. Maintaining high data accuracy is the premise of building information modelling (BIM) usability, so zero watermarking is a hotspot in the research of BIM data security protection. BIM model is a type of 3D model, however, most of the existing zero watermarking algorithms for 3D models are difficult to be better applied to BIM data due to data structure differences. To solve this problem, a zero-watermarking algorithm for BIM data based on distance partitioning and skewness measure is proposed. Firstly, after spatial partitioning based on element paradigm value, the mapping relationship between different partitions and watermarking bits is established. Then, the skewness of elements is calculated, and the skewness measure sign is used as the feature to obtain the binary sequence. Finally, the dissimilarity operation is performed on the binary sequence and the original watermarking sequence which was disordered to construct the zero watermark of the BIM data. The experimental results show that the zero watermarks constructed from different BIM data are unique and robust to translation, rotation, element deletion, element addition, and format conversion attacks. In addition, the superiority of this paper’s algorithm over the comparison algorithm in terms of robustness is compared. Therefore, the proposed algorithm can effectively provide technical support for BIM data copyright protection.

High-Resolution Remote Sensing Image Zero-Watermarking Algorithm Based on Blockchain and SDAE

High-Resolution Remote Sensing Image Zero-Watermarking Algorithm Based on Blockchain and SDAE

Robust Zero Watermarking Algorithm for Medical Images Based on Improved NasNet-Mobile and DCT

In the continuous progress of mobile internet technology, medical image processing technology is also always being upgraded and improved. In this field, digital watermarking technology is significant and provides a strong guarantee for medical image information security. This paper offers a robustness zero watermarking strategy for medical pictures based on an Improved NasNet-Mobile convolutional neural network and the discrete cosine transform (DCT) to address the lack of robustness of existing medical image watermarking algorithms. First, the structure of the pre-training network NasNet-Mobile is adjusted by using a fully connected layer with 128 output and a regression layer instead of the original Softmax layer and classification layer, thus generating a regression network with 128 output, whereby the 128 features are extracted from the medical images using the NasNet-Mobile network with migration learning. Migration learning is then performed on the modified NasNet-Mobile network to obtain the trained network, which is then used to extract medical image features, and finally the extracted image features are subjected to DCT transform to extract low frequency data, and the perceptual hashing algorithm processes the extracted data to obtain a 32-bit binary feature vector. Before performing the watermark embedding, the watermark data is encrypted using the chaos mapping algorithm to increase data security. Next, the zero watermarking technique is used to allow the algorithm to embed and extract the watermark without changing the information contained in the medical image. The experimental findings demonstrate the algorithm’s strong resistance to both conventional and geometric assaults. The algorithm offers some practical application value in the realm of medicine when compared to other approaches.

Robust zero-watermarking algorithm for diffusion-weighted images based on multiscale feature fusion

Robust zero-watermarking algorithm for diffusion-weighted images based on multiscale feature fusion

Geometric attacks resistant double zero-watermarking using discrete Fourier transform and fractional-order Exponent-Fourier moments

Zero-watermarking algorithms provide the means of effective, lossless copyright protection, but the existing ones can only resist a limited variety of geometric attacks. To overcome this limitation, a novel zero-watermarking algorithm resistant to an extensive variety of geometric attacks is suggested, in which fractional-order Exponent-Fourier moments (FrEFMs) and discrete Fourier transform (DFT) are used as complementary geometric invariant features to construct zero-watermarks. Relying on the rotation, scaling, aspect ratio changing and cropping invariance of FrEFMs amplitude and the perfect translation invariance of DFT amplitude, our algorithm can effectively resist a wider variety of geometric and common image processing attacks, which has been experimentally illustrated in this work. Therefore, the proposed zero-watermarking algorithm is superior to other cutting-edge ones.

Adaptive HDR Image Blind Watermarking Approach Based on Redundant Discrete Wavelet Transform

Remarkable success has been recorded in the usage of digital watermarking which is aimed at protecting the intellectual property of multimedia content. In this paper, a new tone mapping attack-resistant high dynamic range (HDR) image zero-watermarking algorithm is proposed. In this algorithm extraction of stable and invariant features are extracted for efficient zero-watermarking through the application of the redundant discrete wavelet transform (RDWT) to the HDR image. The first step involves transforming the HDR image to HVS color space, and RDWT is implemented using the V-channel so that the LL sub-band which contains the strong structure contents of the image is obtained. The second step involves dividing the LL sub-band into non-overlapping blocks, which are afterwards subjected to the process of transformation through the use of the singular value decomposition (SVD) so that the U matrix can be extracted. Third, the use of an Auto-Regressive (AR) prediction technique was employed in generating a local relationship model and comparison is done to facilitate the production of a binary feature mask. In the fourth process, hybrid chaotic mapping (HCM) is used to generate blended watermark so that the security of the watermark can be fortified. Lastly, the computation of an effective zero-watermark is achieved through the implementation of an exclusive-or operation on the blended watermark and the binary feature mask. Based on the results, the approach presented in this study demonstrated superior performance in terms of withstanding TM attacks and other attacks associated with image processing.

A Powerful Zero-Watermarking Algorithm for Copyright Protection of Color Images Based on Quaternion Radial Fractional Hahn Moments and Artificial Bee Colony Algorithm

A Powerful Zero-Watermarking Algorithm for Copyright Protection of Color Images Based on Quaternion Radial Fractional Hahn Moments and Artificial Bee Colony Algorithm

Zero-Watermarking for Vector Maps Combining Spatial and Frequency Domain Based on Constrained Delaunay Triangulation Network and Discrete Fourier Transform.

With its lossless properties, zero-watermarking has attracted a lot of attention in the field of copyright protection for vector maps. However, the common zero-watermarking algorithm puts too much emphasis on mining for global features, making it vulnerable to cropping attacks, and the robustness is not comprehensive enough. This study provides a vector map zero-watermarking scheme that utilizes spatial statistical information and frequency domain transformation methods in an effort to solve the aforementioned issue. In order to make the scheme more resistant to cropping and compression, it is constructed on the basis of feature point extraction and point constraint blocking of the original vector map. Within each sub-block, feature points are used to build constraint Delaunay triangulation networks (CDTN), and the angular values within the triangle networks are then extracted as spatial statistics. The angle value sequence is further transformed by discrete Fourier transform (DFT), and the binarized phase sequence is used as the final feature information to build a zero watermark by executing an exclusive disjunction operation with the encrypted copyright watermark image, both of which contribute to the scheme's robustness and security. The results of the attack experiments show that the proposed vector map zero-watermarking can restore identifiable copyright images under common geometric attacks, cropping attacks, and coordinate system transformations, demonstrating a high level of robustness. The theoretical basis for the robustness of this watermarking scheme is the stability of CDTN and the geometric invariance of DFT coefficients, and both theory and experiment validate the method's validity.

A novel zero-watermarking algorithm based on multi-feature and DNA encryption for medical images

A novel zero-watermarking algorithm based on multi-feature and DNA encryption for medical images

Robust Image Watermarking With Quaternion Fractional-Order Polar Harmonic-Fourier Moments Based On Wavelet Transformation: Resistance Against Rotation Attacks

This study presents a zero-watermarking algorithm that can resist rotation attacks. The algorithm uses quaternion fractional-order polar harmonic-Fourier moments (QFr-PHFMs) based on wavelet-transformation. First, the wavelet-transformation is applied to each component of the host image, which is in RGB three-channel color. The low-frequency sub-bands of each component are then extracted and represented using quaternion algebra. Multiple QFr-PHFMs are calculated, and the invariants of the QFr-PHFMs are utilized to establish the watermark system. The watermark extraction process is also simplified. The detection of the image requires a two-level wavelet transformation, followed by the calculation of multiple invariant moments of the low-frequency sub-image. The experimental results are shown and compared with similar methods. Simulations show that this method can produce high-quality visual effects and withstand noise, filtering, JPEG compression, and cropping attacks.

Application of Zero-Watermarking for Medical Image in Intelligent Sensor Network Security

The field of healthcare is considered to be the most promising application of intelligent sensor networks. However, the security and privacy protection of medical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention. Fortunately, digital watermarking provides an effective method to solve this problem. In order to improve the robustness of the medical image watermarking scheme, in this paper, we propose a novel zero-watermarking algorithm with the integer wavelet transform (IWT), Schur decomposition and image block energy. Specifically, we first use IWT to extract low-frequency information and divide them into non-overlapping blocks, then we decompose the sub-blocks by Schur decomposition. After that, the feature matrix is constructed according to the relationship between the image block energy and the whole image energy. At the same time, we encrypt watermarking with the logistic chaotic position scrambling. Finally, the zero-watermarking is obtained by XOR operation with the encrypted watermarking. Three indexes of peak signal-to-noise ratio, normalization coefficient (NC) and the bit error rate (BER) are used to evaluate the robustness of the algorithm. According to the experimental results, most of the NC values are around 0.9 under various attacks, while the BER values are very close to 0. These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods, which indicates it is more suitable for medical image privacy and security protection.