Explainable face forgery detection requires not only correct judgments but also forensic evidence supported by the input image. However, training cues are typically derived from the forgery generation pipeline. Since images available to the model are of limited quality and resolution, it is difficult to observe the fin...
Bin-Jia Zhou, Da-Wei Luo, Shuai Chen et al.· 0 citations
The Frequency-Aware Multi-Label Learning Network (FMCerNet), a tile-level feature extractor equipped with a Large-Field Frequency-Enhanced ViT backbone and a Coarse-to-Fine multi-label classification head that improves the accuracy of positive screening and fine-grained subtype classification while maintaining high inf...
Lin-Yun Zhou, Qiu-Yu Cai, Xiao-Ling Wang et al.· Bioinformatics· 0 citations
RippleNet is proposed, an AI-generated image detection framework based on local differential signals that adaptively identifies forgery-sensitive regions and constructs multi-directional, multi-scale differential representations within local neighborhoods, explicitly characterizing anomalous patterns in neighborhood st...
Jia-Zhen Yang, Ruijin Jin, Junjun Zheng et al.· 0 citations
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