13 citations · 13 across the 3 of their papers we have counts for
4 papers
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…
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…
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…
Unearthing Common Inconsistency for Generalisable Deepfake Detection
Beilin Chu, Xuan Xu, Weike You +1
Deepfake has emerged for several years, yet efficient detection techniques could generalize over different manipulation methods require further research. While current image-level…