Feature aggregation and region-aware learning for detection of splicing forgery.

Xu, Yanzhi; Zheng, Jiangbin; Ren, Jinchang; Fang, Aiqing

Abstract

Detection of image splicing forgery become an increasingly difficult task due to the scale variations of the forged areas and the covered traces of manipulation from post-processing techniques. Most existing methods fail to jointly multi-scale local and global information and ignore the correlations between the tampered and real regions in inter-image, which affects the detection performance of multi-scale tampered regions. To tackle these challenges, in this paper, we propose a novel method based on feature aggregation and region-aware learning to detect the manipulated areas with varying scales. In specific, we first integrate multi-level adjacency features using a feature selection mechanism to improve feature representation. Second, a cross-domain correlation aggregation module is devised to perform correlation enhancement of local features from CNN and global representations from Transformer, allowing for a complementary fusion of dual-domain information. Third, a region-aware learning mechanism is designed to improve feature discrimination by comparing the similarities and differences of the features between different regions. Extensive evaluations on benchmark datasets indicate the effectiveness in detecting multi-scale spliced tampered regions.

Citation

XU, Y., ZHENG, J., REN, J. and FANG, A. 2024. Feature aggregation and region-aware learning for detection of splicing forgery. IEEE signal processing letters [online], 31, pages 696-700. Available from: https://doi.org/10.1109/LSP.2023.3348689

• Journal Article Type: Article
• Acceptance Date: Oct 31, 2023
• Online Publication Date: Jan 1, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Jan 18, 2024
• Publicly Available Date: Jan 18, 2024
• Journal: IEEE Signal Processing Letters
• Print ISSN: 1070-9908
• Electronic ISSN: 1558-2361
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 31
• Pages: 696-700
• DOI: https://doi.org/10.1109/LSP.2023.3348689
• Keywords: Image forgery detection; Vision transformer; Correlation enhancement; Region-aware learning; Correlation; Feature extraction; Forgery; Learning systems; Splicing; Training; Transformers
• Public URL: https://rgu-repository.worktribe.com/output/2212849