2026· IEEE Signal Processing Letters· Vol 33, pp. 2979-2983· 0 citations· 35 references
Abstract
The rapid advancement of deepfake image generation poses significant threats to information security and social trust, with forgery artifacts exhibiting multi-scale characteristics from micro-level noise to macro-level semantic anomalies.Existing detection methods are limited by single-scale feature extraction and inefficient fusion due to semantic gaps between deep and shallow features.To address these issues, this letter proposes a Progressive Multi-Level Collaborative Framework (PMCF) comprising a multi-granularity collaborative attention (MGCA) module and a multi-level attention fusion (MLAF) module. MGCA uses three parallel Transformer branches to model pixel-level textures, local structures, and global semantics, while MLAF integrates a feature pyramid, joint attention, and progressive bidirectional fusion for effective feature alignment. Experimental results show consistent AUC improvements over comparative methods across multiple datasets.
This paper proposes an image forgery detection and localization method based on Multilevel Feature Collaboration Bridge (Loupe), using Vision Transformer as the backbone to extract multi-level features and designs a Feature Collaboration Bridge Network to realize efficient information interaction between image-level cl...
The increasing realism of image manipulations poses significant challenges for forgery localization. However, existing methods are hindered by the limited adaptability of constrained frequency filters and the dilution of subtle forensic cues in deep networks. To address these challenges, we propose the Laplacian pyrami...
Zhuo-Fei Liu, Wen-Jie Li, Yang Yu· IEEE Signal Processing Lette...· 0 citations
These findings show that complementary visual and metadata cues are much more useful in detection, while the use of a lightweight backbone enables efficient, high-throughput forensic analysis suitable for real-world deployment.
Baysah Guwor, Mohammad Shabaz· Multimedia· 0 citations
Experimental evaluations on benchmark forensic datasets demonstrate that the proposed framework achieves competitive detection performance and robustness across most datasets, attaining an AUC of up to 0.998 on NIST16, while highlighting specific failure modes on challenging cases such as COVERAGE.
Mahran M. Al-Zyoud, M. A. Jabbar, M. S. Heera et al.· Journal of Computer Virology...· 0 citations
Findings confirm that the proposed MAFN-HFL is a scalable and efficient solution for next-generation image forgery detection, and shows strong performance on two benchmark datasets.
Rapelli Srikanth, Suresh Kumar Mandala· International journal of pat...· 0 citations
This paper proposes an end-to-end Transformer-based framework, termed Progressively Explicit Query Network (PEQNet), for multi-face forgery detection and localization, and introduces triple contrastive learning to model the mutual exclusivity among real, fake, and background regions.
Peng-Wen Dai, Xiaomeng Wen, Feiyang He et al.· ACM Transactions on Multimed...· 0 citations
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