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

Guokai Wang; Liuping Feng; Lingyi Chi; Yangquan Zhou

doi:10.1117/12.3014419

9 January 2024 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, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 1296909 (2024) https://doi.org/10.1117/12.3014419
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guokai Wang, Liuping Feng, Lingyi Chi, and Yangquan Zhou "A self-adaptive tampering detection algorithm based on image segmentation and feature point matching", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 1296909 (9 January 2024); https://doi.org/10.1117/12.3014419

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