5 papers
Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture
Chenqi Kong, Anwei Luo, Peijun Bao +5
Open-set face forgery detection poses significant security threats and presents substantial challenges for existing detection models. These detectors primarily have two limitations…
Variation-Bounded Loss for Noise-Tolerant Learning
Jialiang Wang, Xiong Zhou, Xianming Liu +4
Mitigating the negative impact of noisy labels has been aperennial issue in supervised learning. Robust loss functions have emerged as a prevalent solution to this problem. In this…
Propose and Rectify: A Forensics-Driven MLLM Framework for Image Manipulation Localization
Keyang Zhang, Chenqi Kong, Hui Liu +3
The increasing sophistication of image manipulation techniques demands robust forensic solutions that can both reliably detect alterations and precisely localize tampered regions.…
MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection
Chenqi Kong, Anwei Luo, Peijun Bao +5
Deepfakes have recently raised significant trust issues and security concerns among the public. Compared to CNN face forgery detectors, ViT-based methods take advantage of the expr…
Temporal Unlearnable Examples: Preventing Personal Video Data from Unauthorized Exploitation by Object Tracking
Qiangqiang Wu, Yi Yu, Chenqi Kong +5
With the rise of social media, vast amounts of user-uploaded videos (e.g., YouTube) are utilized as training data for Visual Object Tracking (VOT). However, the VOT community has l…