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Video hashing is an efficient technique for tasks like copy detection and retrieval. This paper utilizes canonical polyadic (CP) decomposition and Hahn moments to design a robust video hashing. The first significant contribution is the secondary frame construction. It uses three weighted techniques to generate three secondary frames for each video group, which can effectively capture features of video frames from different aspects and thus improves discrimination. Another contribution is the deep feature extraction via the ResNet50 and CP decomposition. The use of the ResNet50 can provide rich features and the CP decomposition can learn a compact and discriminative representation from the rich features. In addition, the Hahn moments of secondary frames are taken to construct hash elements. Extensive experiments on the open video dataset demonstrate that the proposed algorithm surpasses several state-of-the-art algorithms in balancing discrimination and robustness. [ABSTRACT FROM AUTHOR]

We propose a novel image encryption scheme that utilizes chaotic maps and random scan patterns to protect image content. Our scheme includes three stages: pixel permutation via random scan patterns, block pixel diffusion, and block permutation. In the first stage, the input image is partitioned into overlapping blocks, and random scan patterns controlled by chaotic maps are exploited to scramble pixels of blocks. In the second stage, content-based keys are generated from the input image, and the content-based keys and chaotic maps are used to calculate secure matrices for achieving diffusion. The block pixel diffusion is conducted by performing element-wise XOR operation between blocks and their corresponding secure matrices. In the third stage, the image is partitioned into non-overlapping blocks, and all blocks are randomly permuted to further improve security. Key space analysis shows that the key space of the proposed scheme is bigger than those of some existing schemes. Experiments on standard test images are conducted by using some well-known metrics, such as histogram, χ2 test, correlation coefficient, entropy, the number of pixels change rate, the unified average changing intensity, and peak signal-to-noise ratio. The results demonstrate that the proposed scheme reaches the state-of-the-art performance in security and speed. [ABSTRACT FROM AUTHOR]

A reversible data hiding (RDH) method with prediction error histogram (PEH) decomposition and hybrid dimensional histogram modification is proposed. First, a global one-dimensional (1D) PEH is generated by pixel-based pixel value ordering predictor. The proposed method fully utilizes the pixels inside the range of the minimum and maximum of neighbors to decompose 1D-PEH into several sub 1D-PEHs. Then, each sub 1D-PEH is separated into two parts. The part with small-magnitude is used to construct a reduced two-dimensional (2D) PEH, whereas the rest is shifted for reversibility. Finally, data embedding is performed by modifying each reduced 2D-PEH to reduce distortion. Experimental results demonstrates that the proposed method outperforms some high-fidelity RDH methods. [ABSTRACT FROM AUTHOR]