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cs.CV2025
When Semantics Regulate: Rethinking Patch Shuffle and Internal Bias for Generated Image Detection with CLIP
Beilin Chu, Weike You, Mengtao Li +7
The rapid progress of GANs and Diffusion Models poses new challenges for detecting AI-generated images. Although CLIP-based detectors exhibit promising generalization, they often r…
cs.CV2025
FIRE: Robust Detection of Diffusion-Generated Images via Frequency-Guided Reconstruction Error
Beilin Chu, Xuan Xu, Xin Wang +3
The rapid advancement of diffusion models has significantly improved high-quality image generation, making generated content increasingly challenging to distinguish from real image…
cs.CV2025
Reduced Spatial Dependency for More General Video-level Deepfake Detection
Beilin Chu, Xuan Xu, Yufei Zhang +2
As one of the prominent AI-generated content, Deepfake has raised significant safety concerns. Although it has been demonstrated that temporal consistency cues offer better general…