3 citations · 7 across the 4 of their papers we have counts for
4 papers
C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detection
Chuangchuang Tan, Renshuai Tao, Huan Liu +4
This work focuses on AIGC detection to develop universal detectors capable of identifying various types of forgery images. Recent studies have found large pre-trained models, such…
Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning
Chuangchuang Tan, Yao Zhao, Shikui Wei +3
This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images despite limited training data. Existing frequ…
Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection
Chuangchuang Tan, Huan Liu, Yao Zhao +4
Recently, the proliferation of highly realistic synthetic images, facilitated through a variety of GANs and Diffusions, has significantly heightened the susceptibility to misuse. W…
LID 2020: The Learning from Imperfect Data Challenge Results
Yunchao Wei, Shuai Zheng, Ming-Ming Cheng +32
Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotate…