5 papers
When Diffusion Models Forget Who You Are: Identity Preservation in Face Inpainting under Large Occlusions
Feng Ding, Shuhuai Xie, Yue Zhou +3
Face inpainting with diffusion models has recently achieved impressive visual quality, yet preserving identity fidelity under significant occlusion and conflicting text guidance re…
DiffFace-Edit: A Diffusion-Based Facial Dataset for Forgery-Semantic Driven Deepfake Detection Analysis
Feng Ding, Wenhui Yi, Xinan He +3
Generative models now produce imperceptible, fine-grained manipulated faces, posing significant privacy risks. However, existing AI-generated face datasets generally lack focus on…
Redundant Semantic Environment Filling via Misleading-Learning for Fair Deepfake Detection
Xinan He, Yue Zhou, Shu Hu +3
Detecting falsified faces generated by Deepfake technology is essential for safeguarding trust in digital communication and protecting individuals. However, current detectors often…
Decoupling Forgery Semantics for Generalizable Deepfake Detection
Wei Ye, Xinan He, Feng Ding
In this paper, we propose a novel method for detecting DeepFakes, enhancing the generalization of detection through semantic decoupling. There are now multiple DeepFake forgery tec…
FairAdapter: Detecting AI-generated Images with Improved Fairness
Feng Ding, Jun Zhang, Xinan He +1
The high-quality, realistic images generated by generative models pose significant challenges for exposing them.So far, data-driven deep neural networks have been justified as the…