A novel zero watermark optimization algorithm based on Gabor transform and discrete cosine transform

Abstract

Zero watermark has no intervention on the carrier image, which is invisible in nature, and completely solves the problem of mutual constraint between robustness and invisibility in the traditional digital watermark technology. In order to improve the robustness of the zero watermark, a zero watermark algorithm based on Gabor transform and discrete cosine transform (DCT) is proposed in this paper. The algorithm performs Gabor transformation on the carrier image to obtain the directional characteristics of the image. Then it performs DCT and blocks, and performs singular value decomposition (SVD) on each subblock. The feature matrix is constructed using the largest eigenvalue of the subblock, and then XORed with the encrypted watermark to form a zero watermark. The algorithm utilizes the advantages of Gabor transform in the adjustment of scale and direction and selects the optimized parameters to extract image features. At the same time, the information concentration and decorrelation of DCT and SVD are utilized to improve the robustness and antiattack capability of the algorithm. The experimental results show that the proposed algorithm is robust to filtering, JPEG compression, noise, shearing, rotation, and other attacks, especially effective in antishear attack and rotation attack.

Encryption Based Image Watermarking Algorithm in 2DWT-DCT Domains

“…Singh et al [ 41 ] proposed a combined DWT–SVD method that divided an image into the two least and most significant bits for embedding, and this strategy was tested under some common image attacks, e.g., histogram equalization operations, and the BCR results were comparatively lower for the corrected bit rate. Fan et al [ 42 ] proposed an algorithm based on the Gabor transformation and discrete cosine transform. This algorithm used Gabor transformation due to its scaling, direction, and optimization capabilities in image adjustment.…”

“…It has been observed that the majority of the existing methods are incapable of the satisfactory recovery of the watermark while ensuring minimal image disturbance. For example, Fares et al [ 31 ] achieved 91% recovery for a salt and pepper attack, and Fan et al [ 42 ] obtained 99.69% recovery for a Gaussian noise attack. When it comes to peer-to-peer communication or precise data retrieval, a single bit failure can cause total authentication failure.…”

Video Watermarking Algorithm Based on NSCT, Pseudo 3D-DCT and NMF

“…At present, the research of digital watermarking technology for video is mainly based on spatial domain [4][5][6], compression domain [7][8][9][10][11][12] and transform domain [13][14][15][16][17][18][19][20][21]. The principle of the video watermarking algorithm in the spatial domain is to embed watermark data on the basis of a processing pixel value of a video frame image.…”

“…This kind of algorithm is designed to embed and extract the watermark in the transform domain. DWT (Discrete Wavelet Transform) [13], DCT (Discrete Cosine Transform) [14] and SVD (Singular Value Decomposition) [15] are commonly used to transform the image into the transformation domain, which then enables us to embed the watermark in the transform domain. Combining graph-based transformation, singular value decomposition and hyperchaotic encryption, Sharma et al [16] proposed a video watermarking algorithm, which can solve the address quality loss of data well; however, the algorithm is complex, and the anti-rotation attack performance is poor.…”

Audio and Video Matching Zero‐Watermarking Algorithm Based on NSCT

“…e media attached to the algorithm are single. Image digital watermarking mainly includes spatial domain method [1], transform domain method [2][3][4][5][6], and deep learning-based method [7]. Transform domain method commonly uses DCT (Discrete Cosine Transform), NSCT (Nonsubsampled Contourlet Transform) [4,5], DWT (Discrete Wavelet Transform) [6], and so on.…”