Document image forgery detection based on multiscale edge similarity

Feiyang Sun; Lin Sun; Jian Wang

doi:10.1117/12.3035324

19 July 2024 Document image forgery detection based on multiscale edge similarity

Feiyang Sun, Lin Sun, Jian Wang

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132132S (2024) https://doi.org/10.1117/12.3035324
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Document images often contain critical textual information, and once manipulated or edited, they can cause significant harm to society when propagated on the internet. To date, although some works have achieved research results in the field of document image forgery detection, they have not fully explored edge features, which are important feature information in document images. In response to this research status, we propose a new document image forgery detection method based on multiscale edge similarity (MES). The proposed method employs multiscale difference of Gaussian (MDOG) to calculate the edge structure similarity, edge contrast similarity and edge information entropy at each pixel between the image to be detected and the reference image at different scales. These computed results are combined to generate an edge similarity distribution map, which is used to pinpoint the specific location of the forged area. Extensive experimental results demonstrate that the proposed method outperforms some of the current state-of-the-art forgery detection methods and shows robustness against several common postprocessing operations.

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

Citation Download Citation

Feiyang Sun, Lin Sun, and Jian Wang "Document image forgery detection based on multiscale edge similarity", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132132S (19 July 2024); https://doi.org/10.1117/12.3035324

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