22 citations · 57 across the 12 of their papers we have counts for
31 papers · 1 filter
Generalizable Face Forgery Detection via Separable Prompt Learning
Enrui Yang, Baoyuan Wu, Yuezun Li
Detecting face forgeries using CLIP has recently emerged as a promising direction. However, most existing methods focus on adapting its visual encoder, leaving the potential of the…
Detecting Diffusion-generated Images via Dynamic Assembly Forests
Mengxin Fu, Yuezun Li
Diffusion models are known for generating high-quality images, causing serious security concerns. To combat this, most efforts rely on deep neural networks (e.g., CNNs and Transfor…
Off-the-shelf Vision Models Benefit Image Manipulation Localization
Zhengxuan Zhang, Keji Song, Junmin Hu +2
Image manipulation localization (IML) and general vision tasks are typically treated as two separate research directions due to the fundamental differences between manipulation-spe…
Celeb-DF++: A Large-scale Challenging Video DeepFake Benchmark for Generalizable Forensics
Yuezun Li, Delong Zhu, Xinjie Cui +1
The rapid advancement of AI technologies has significantly increased the diversity of DeepFake videos circulating online, posing a pressing challenge for \textit{generalizable fore…
Hiding Faces in Plain Sight: Defending DeepFakes by Disrupting Face Detection
Delong Zhu, Yuezun Li, Baoyuan Wu +3
Face-swapping DeepFakes have become an escalating societal concern, attracting increasing attention in recent years. To counter this, we investigate a new proactive defense framewo…
Forensics Adapter: Unleashing CLIP for Generalizable Face Forgery Detection
Xinjie Cui, Yuezun Li, Delong Zhu +3
We describe Forensics Adapter, an adapter network designed to transform CLIP into an effective and generalizable face forgery detector. Although CLIP is highly versatile, adapting…