6 papers
Generalizable Face Forgery Detection via Separable Prompt Learning
Enrui Yang, Yuezun Li
Detecting face forgeries using CLIP has recently emerged as a promising and increasingly popular research direction. Owing to its rich visual knowledge acquired through large-scale…
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…
Generalizing Video DeepFake Detection by Self-generated Audio-Visual Pseudo-Fakes
Zihe Wei, Yuezun Li
Detecting video deepfakes has become increasingly urgent in recent years. Given the audio-visual information in videos, existing methods typically expose deepfakes by modeling cros…
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…
Texture, Shape, Order, and Relation Matter: A New Transformer Design for Sequential DeepFake Detection
Yunfei Li, Yuezun Li, Baoyuan Wu +3
Sequential DeepFake detection is an emerging task that predicts the manipulation sequence in order. Existing methods typically formulate it as an image-to-sequence problem, employi…
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…