Ms. Yangquan Zhou Profile

Yangquan Zhou

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Proceedings Article | 9 January 2024 Paper

A self-adaptive tampering detection algorithm based on image segmentation and feature point matching

Guokai Wang, Liuping Feng, Lingyi Chi, Yangquan Zhou

Proceedings Volume 12969, 1296909 (2024) https://doi.org/10.1117/12.3014419

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In order to enhance the efficiency and accuracy of homologous tampering detection, image segmentation algorithms and image feature points are combined. The Simple Linear Iterative Cluster (SLIC) algorithm is employed for image segmentation. However, manually presetting the number of patches is not applicable to all images and can influence subsequent segmentation results. To achieve a more accurate detection of tampered areas, this paper proposes a self adaptive image tampering detection algorithm. The number of image segments is determined based on image complexity, which allows the image to be segmented into semantically independent patches. Subsequently, the SIFT algorithm is employed to extract feature points for matching. Test results demonstrate that the proposed algorithm accurately localizes tampered regions and reduces algorithmic complexity.

Proceedings Article | 14 November 2023 Paper

A reversible data hiding algorithm based on image region division

Lingyi Chi, Liuping Feng, Yangquan Zhou, Guokai Wang

Proceedings Volume 12934, 129341W (2023) https://doi.org/10.1117/12.3008020

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Many reversible data hiding algorithms only scan images in a fixed sequence, leading to low embedding capacity and poor stego-image quality. This paper proposes an adaptive algorithm that divides images into smooth and texture regions based on complexity, and uses different embedding algorithms for each region. Three predictors are used to generate asymmetric histograms and increase embedding capacity through two rounds of embedding in smooth regions. Pixel value ordering (PVO) is used for embedding fewer data in texture regions to maintain image quality. Experimental results show that the algorithm has high embedding capacity and low visual distortion.

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