Zero-watermarking Algorithm Research Articles

A zero-watermarking algorithm for vector geographic data based on feature invariants

A zero-watermarking algorithm for vector geographic data based on feature invariants

Zero-Watermarking Algorithm for Remote Sensing Image via BEMD and DFT

In order to solve the problem of modifying the host image when the traditional image watermarking algorithm protects the copyright of remote sensing image, and to improve the robustness of remote sensing image watermarking algorithm, a remote sensing image zero watermarking algorithm based on bi-dimensional empirical mode decomposition (BEMD) and discrete Fourier transform (DFT) is proposed. Firstly, BEMD is used to decompose the host image to get the intrinsic mode function and margin, and the residual information is divided into 8×8 blocks DFT, and the high frequency coefficients are constructed into coefficient images. Then Arnold transform is used to scramble the watermark image to improve the security of the watermark image. Then the size of the watermark image is adjusted to solve the problem of image size matching between the host image and the watermark image in the process of watermark embedding. Finally, the coefficient image decomposed by DFT and the watermark image are constructed into feature image by XOR operation, and the construction of zero watermark in remote sensing image is realized. The experimental results of a large number of remote sensing images and watermark images show that the proposed algorithm can effectively resist all kinds of attacks such as filtering, sharpening, histogram equalization, Gamma transform, noise, scaling and so on. In the case of 12 attacks and 60 parameters, 95.0% of the extracted watermark images have a normalized correlation value of more than 0.990 with the original watermark image, compared with the traditional remote sensing image watermarking algorithm. The algorithm ensures the harmlessness of the host image, so that the feature image saved in the notary office for copyright protection verification will not cause any damage to the host image, and has better robustness.

Robust medical zero‐watermarking algorithm based on Residual‐DenseNet

To solve the problem of poor robustness of existing traditional DCT-based medical image watermarking algorithms under geometric attacks, a novel deep learning-based robust zero-watermarking algorithm for medical images is proposed. A Residual-DenseNet is designed, which took low-frequency features after discrete cosine transformation of medical images as labels and applied skip connections and a new objective function to strengthen and extract high-level semantic features that can effectively distinguish different medical images and binarise them to get robust hash vectors. Then, these hash vectors are bound with the chaotically encrypted watermark to generate the corresponding keys to complete the generation of watermark. The proposed algorithm neither modified the original medical image in the watermark generation stage nor required the original medical image in the watermark extraction stage. Moreover, the proposed algorithm is also suitable for multiple watermarks. Experimental results show that the proposed algorithm has good robust performance under both conventional and geometric attacks.

Robust Zero-Watermarking of Color Medical Images Using Multi-Channel Gaussian-Hermite Moments and 1D Chebyshev Chaotic Map.

Copyright protection of medical images is a vital goal in the era of smart healthcare systems. In recent telemedicine applications, medical images are sensed using medical imaging devices and transmitted to remote places for screening by physicians and specialists. During their transmission, the medical images could be tampered with by intruders. Traditional watermarking methods embed the information in the host images to protect the copyright of medical images. The embedding destroys the original image and cannot be applied efficiently to images used in medicine that require high integrity. Robust zero-watermarking methods are preferable over other watermarking algorithms in medical image security due to their outstanding performance. Most existing methods are presented based on moments and moment invariants, which have become a prominent method for zero-watermarking due to their favorable image description capabilities and geometric invariance. Although moment-based zero-watermarking can be an effective approach to image copyright protection, several present approaches cannot effectively resist geometric attacks, and others have a low resistance to large-scale attacks. Besides these issues, most of these algorithms rely on traditional moment computation, which suffers from numerical error accumulation, leading to numerical instabilities, and time consumption and affecting the performance of these moment-based zero-watermarking techniques. In this paper, we derived multi-channel Gaussian–Hermite moments of fractional-order (MFrGHMs) to solve the problems. Then we used a kernel-based method for the highly accurate computation of MFrGHMs to solve the computation issue. Then, we constructed image features that are accurate and robust. Finally, we presented a new zero-watermarking scheme for color medical images using accurate MFrGHMs and 1D Chebyshev chaotic features to achieve lossless copyright protection of the color medical images. We performed experiments where their outcomes ensure the robustness of the proposed zero-watermarking algorithms against various attacks. The proposed zero-watermarking algorithm achieves a good balance between robustness and imperceptibility. Compared with similar existing algorithms, the proposed algorithm has superior robustness, security, and time computation.

Publisher Correction: A novel zero-watermarking algorithm based on robust statistical features for natural images

Publisher Correction: A novel zero-watermarking algorithm based on robust statistical features for natural images

Image Copyright Protection Based on Blockchain and Zero-Watermark

Generally, image protection is often accomplished by digital watermark, which is also widely used in the field of data protection and data ownership authentication. However, the existing digital watermark has inherent disadvantages, it needs completely trusted third parties to act as the arbitration parties, which may be difficult to achieve. Second, an image watermark algorithm will inevitably lead to the loss of image data when operating on the image data, while it is usually irreversible. The zero-watermark algorithm can solve the problem of data loss but relies more on the trusted third party than a traditional digital watermark, which makes its prospects limited. With the development of blockchain technology in recent years, its features of de-trusted third parties combine the fairness and process automation of smart contracts to make up for the shortcomings of the zero-watermarking algorithm. This paper studies image storage and authentication frameworks. This framework utilizes the advantages of zero-watermarking algorithm and blockchain, using the Inter-Planetary File System to solve the data expansion problem of blockchain, which well avoids the shortcomings of zero-watermarking algorithm and blockchain. Experiment and inference illustrate that the proposed framework is feasible.

A zero-watermark algorithm for multiple images based on visual cryptography and image fusion

At present, it is difficult for the multiple images zero-watermark algorithm to protect all the images in the image set, and repeated operations will reduce the efficiency of the algorithm. To solve these issues, the proposed algorithm can design a reasonable copyright protection scheme according to the number of images in the image set to realize the protection of all images, and reduce the cost of time and storage. The gray-weighted average image fusion method is used to fuse multiple normalized standard images into one image. The LWT(Lifting the Wavelet Transform)-QR decomposition is applied to the effective area of the fusion image to obtain the robust feature image. Non-extended visual cryptography is used to enhance the security of the algorithm. A zero-watermark image is obtained by using the XOR manipulation for the feature image and the public shared image. Experimental results demonstrate that the proposed algorithm has good performance.

A novel zero-watermarking algorithm based on robust statistical features for natural images

In this paper, we develop a robust image zero-watermarking algorithm based on multiple circular statistical features. First of all, we detect stable feature points from the host image, and construct circular region partitions from the identified feature points, which contain the critical information of the host image. Second, the statistics and differences of circular region partitions are calculated, which contain both the local differences and the global information of the host image. Third, we apply discrete cosine transform (DCT) and singular value decomposition (SVD) in statistics and differences in order to construct the feature image. The stability of DCT and SVD further warrants the robustness of the algorithm. Moreover, Arnold transform is used to scramble the watermark image to enhance the security of the algorithm. Finally, we obtain the zero watermark by performing the exclusive-or (XOR) operation between the feature image and the scrambled watermark image, and the zero watermark is stored in the copyright authentication database for copyright authentication. Moreover, to combat geometric attacks, we develop a blind image correction algorithm without using the original image to accurately correct the attacked image. Numerous experiments and comparisons with the state-of-the-art (SOTA) watermarking algorithms confirm that the proposed algorithm affords good robustness against various attacks, such as filtering attacks, noise attacks, geometric attacks, and even a random combination of the above-stated attacks.

Robust Zero-Watermarking Algorithm for Medical Images Using Double-Tree Complex Wavelet Transform and Hessenberg Decomposition

With the rapid development of smart medical care, copyright security for medical images is becoming increasingly important. To improve medical images storage and transmission safety, this paper proposes a robust zero-watermarking algorithm for medical images by fusing Dual-Tree Complex Wavelet Transform (DTCWT), Hessenberg decomposition, and Multi-level Discrete Cosine Transform (MDCT). First, the low-frequency sub-band of the medical image is obtained through the DTCWT and MDCT. Then Hessenberg decomposition is used to construct the visual feature vector. Meanwhile, the encryption of the watermarking image by combining cryptographic algorithms, third-party concepts, and chaotic sequences enhances the algorithm’s security. In the proposed algorithm, zero-watermarking technology is utilized to assure the medical images’ completeness. Compared with the existing algorithms, the proposed algorithm has good robustness and invisibility and can efficiently extract the watermarking image and resist different attacks.

Stereoscopic Image Description With Trinion Fractional-Order Continuous Orthogonal Moments

Some research progress has been made on fractional-order continuous orthogonal moments (FrCOMs) in the past two years. Compared with integer-order continuous orthogonal moments (InCOMs), FrCOMs increase the number of affine invariants and effectively improve numerical stability. However, the existing types of FrCOMs are still very limited, of which all are planar image oriented. No report on stereoscopic images is available yet. To this end, in this paper, FrCOMs corresponding to various types of InCOMs are first deduced, and then, they are combined with trinion theory to construct trinion FrCOMs (TFrCOMs) applicable to stereoscopic images. Furthermore, the reconstruction performance and geometric invariance of TFrCOMs are analyzed theoretically and experimentally. Finally, an application in the stereoscopic image zero-watermarking algorithm is investigated to verify the superior performance of TFrCOMs.

A novel zero-watermarking scheme based on NSCT-SVD and blockchain for video copyright

With the advent of information era, the issues on video copyright continue to emerge. In the grey industrial chain of piracy, the film industry is suffering a major disaster area. The current protection schemes for video copyright are inefficient and costly so that video industry still has to face the infringement. To address poor robustness, weak imperceptibility and difficulty in traceability of the current protection schemes for video copyright, this paper proposes a novel zero-watermarking scheme based on NSCT-SVD and blockchain for video copyright. In addition, we research the feasibility and reason of combining zero-watermarking with blockchain. The designed zero-watermarking algorithm is based on NSCT-SVD, Arnold Transform and key-frames extraction algorithm which is based on distance threshold clustering. The results show the extracted features from video are stable and secure than DWT-SVD based and the past non-blocking. The performance of the blockchain scheme designed for video copyright practices well. Comparatively, the discussion and results showed that the proposed scheme has been verified more credible and efficient in authoritative traceability compared to trusted third party based. The registration information cannot be tampered and be more suitable for copyright protection.

Robust zero-watermarking algorithm for medical images based on SIFT and Bandelet-DCT

Robust zero-watermarking algorithm for medical images based on SIFT and Bandelet-DCT

Zero-watermarking Algorithm for Audio and Video Matching Verification

<abstract> <p>For the needs of tamper-proof detection and copyright identification of audio and video matching, this paper proposes a zero-watermark algorithm that can be used for audio and video matching verification. The algorithm segments audio and video in smaller time units, generates a video frame feature matrix based on NSCT, DCT, and SVD, and generates a sound watermark based on methods such as DWT and K-means. The zero watermark combines video, audio and copyright information. The experimental results show that the zero watermark generated by this algorithm can not only realize highly accurate matching detection and positioning of audio and video, but also well resist common single attack and combination attacks such as noise, scaling, rotation, frame attack and format conversion, which has good robustness.</p> </abstract>

Robust zero-watermarking algorithm for medical images based on <em>K</em>-means and DCT

Robust zero-watermarking algorithm for medical images based on <em>K</em>-means and DCT

Robust zero-watermarking algorithm for medical images based on Hadamard-DWT-DCT

Robust zero-watermarking algorithm for medical images based on Hadamard-DWT-DCT

Color Zero-Watermarking Algorithm for Medical Images Based on BEMD-Schur Decomposition and Color Visual Cryptography

With the widespread use of medical images in telemedicine, personal information may be leaked. The traditional zero-watermarking technology has poor robustness under large-scale attacks. At the same time, most of the zero-watermarking information generated is a binary sequence with a single information structure. In order to effectively solve the poor robustness problem of traditional zero-watermarking under large-scale attacks, a color zero-watermarking algorithm for medical images based on bidimensional empirical mode decomposition (BEMD)-Schur decomposition and color visual cryptography is proposed. Firstly, the color carrier image and the color copyright logo are decomposed into R, G, and B three color components, respectively, and the feature value of each sub-block are extracted by wavelet transform, BEMD decomposition, block operation, and Schur decomposition. Then, the R, G, and B components of the copyright logo are scrambled by Arnold scramble and converted into binary watermark information. Finally, a color visual cryptography scheme is proposed to generate two color shared images based on the carrier characteristics and copyright information. One shared image is used to generate a color zero-watermark, and the other is used for copyright authentication phase. Experimental results show that this algorithm has strong robustness and stability in resisting large-scale noise attacks, filtering attacks, JPEG compression, cropping attacks, and translation attacks at different positions. Compared with similar zero-watermarking algorithms, the robust performance is improved by about 10%, and it can adapt to more complex network environments.

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

Digital medical system not only facilitates the storage and transmission of medical information but also brings information security problems. Aiming at the security of medical images, a robust zero watermarking algorithm for medical images based on Zernike-DCT is proposed. The algorithm first uses a chaotic logic sequence to preprocess and encrypt the watermark, then performs edge detection and Zernike moment processing on the original medical image to get the accurate edge points, and then performs discrete cosine transform (DCT) on them to get the feature vector. Finally, it combines perceptual Hash and zero watermark technology to generate the key to complete the watermark embedding and extraction. The algorithm has good robustness to conventional and geometric attacks, strong antinoise ability, high positioning accuracy, and processing efficiency and is superior to the classical edge detection algorithm in extraction effect. It is a stable and reliable image edge detection algorithm.

Zero-watermarking algorithm based on Shearlet transform and Hu moment

Digital watermarking technology is widely used in copyright protection. To enhance the robustness as well as the imperceptibility of the watermarking algorithm while taking resist geometric attacks and non-geometric attacks into consideration, this paper proposed a zero-watermarking algorithm based on Shearlet transform and Hu moments for better realizing copyright protection. First Carry out Shearlet transform and block discrete cosine transform on the carrier image. Then calculate the Hu moment in blocks to construct the feature matrix, and construct the zero-watermark. The algorithm improves the security of watermarking images with Kent chaotic map encryption. To test the algorithm, high-intensity general attacks and their combination attacks are performed on images of different textures. The experimental results show that the normalized cross-correlation values all breaking 0.99 and the peak signal-to-noise ratio all breaking 75.80. The scheme not only has perfect imperceptibility but also can effectively resist high-intensity general attacks and their combination attacks, and has strong robustness.

New geometrically invariant multiple zero-watermarking algorithm for color medical images

The single watermarking algorithm for medical images faced several inherent problems like lack of security. In this paper, a new geometrically invariant multiple zero-watermarking method is proposed to secure medical images. We proposed a novel set of multi-channels shifted Gegenbauer moments of fractional orders (FrMGMs). These moments are used to extract the geometrically invariant features from the color medical images. Then we construct a featured image of the original medical image using the magnitude of the selected precise FrMGMs moments. Finally, we applied a scrambling method to scramble the watermark image and then the exclusive OR operation to the images' feature and scrambled watermark to construct a zero-watermark. Experimental results proved that the proposed approach effectively provided better robustness to various standard attacks and outperformed the existing single-, dual-, & triple-zero-watermarking algorithms for medical images.

A novel robust zero-watermarking algorithm for audio based on sparse representation

Behind the prevalence of multimedia technology, digital copyright disputes are becoming increasingly serious. The digital watermarking prevention technique against the copyright infringement needs to be improved urgently. Among the proposed technologies, zero-watermarking has been favored recently. In order to improve the robustness of the zero-watermarking, a novel robust audio zerowatermarking method based on sparse representation is proposed. The proposed scheme is mainly based on the K-singular value decomposition (K-SVD) algorithm to construct an optimal over complete dictionary from the background audio signal. After that, the orthogonal matching pursuit (OMP) algorithm is used to calculate the sparse coefficient of the segmented test audio and generate the corresponding sparse coefficient matrix. Then, the mean value of absolute sparse coefficients in the sparse matrix of segmented speech is calculated and selected, and then comparing the mean absolute coefficient of segmented speech with the average value of the selected coefficients to realize the embedding of zero-watermarking. Experimental results show that the proposed audio zero-watermarking algorithm based on sparse representation performs effectively in resisting various common attacks. Compared with the baseline works, the proposed method has better robustness.