6 papers
Decoupling Bias, Aligning Distributions: Synergistic Fairness Optimization for Deepfake Detection
Feng Ding, Wenhui Yi, Yunpeng Zhou +3
Fairness is a core element in the trustworthy deployment of deepfake detection models, especially in the field of digital identity security. Biases in detection models toward diffe…
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…
VLForgery Face Triad: Detection, Localization and Attribution via Multimodal Large Language Models
Xinan He, Yue Zhou, Bing Fan +3
Faces synthesized by diffusion models (DMs) with high-quality and controllable attributes pose a significant challenge for Deepfake detection. Most state-of-the-art detectors only…
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…