4 citations · 5 across the 10 of their papers we have counts for
10 papers
MoGen: A Unified Collaborative Framework for Controllable Multi-Object Image Generation
Yanfeng Li, Yue Sun, Keren Fu +5
Existing multi-object image generation methods face difficulties in achieving precise alignment between localized image generation regions and their corresponding semantics based o…
On the Holistic Approach for Detecting Human Image Forgery
Xiao Guo, Jie Zhu, Anil Jain +1
The rapid advancement of AI-generated content (AIGC) has escalated the threat of deepfakes, from facial manipulations to the synthesis of entire photorealistic human bodies. Howeve…
Consolidating Diffusion-Generated Video Detection with Unified Multimodal Forgery Learning
Xiaohong Liu, Xiufeng Song, Huayu Zheng +3
The proliferation of videos generated by diffusion models has raised increasing concerns about information security, highlighting the urgent need for reliable detection of syntheti…
TalkingHeadBench: A Multi-Modal Benchmark & Analysis of Talking-Head DeepFake Detection
Xinqi Xiong, Prakrut Patel, Qingyuan Fan +6
The rapid advancement of talking-head deepfake generation fueled by advanced generative models has elevated the realism of synthetic videos to a level that poses substantial risks…
Benchmarking Unified Face Attack Detection via Hierarchical Prompt Tuning
Ajian Liu, Haocheng Yuan, Xiao Guo +13
PAD and FFD are proposed to protect face data from physical media-based Presentation Attacks and digital editing-based DeepFakes, respectively. However, isolated training of these…
Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector
Xiao Guo, Xiufeng Song, Yue Zhang +2
Deepfake detection is a long-established research topic vital for mitigating the spread of malicious misinformation. Unlike prior methods that provide either binary classification…
Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction
Xiao Guo, Manh Tran, Jiaxin Cheng +1
The text-to-image (T2I) personalization diffusion model can generate images of the novel concept based on the user input text caption. However, existing T2I personalized methods ei…
On Learning Multi-Modal Forgery Representation for Diffusion Generated Video Detection
Xiufeng Song, Xiao Guo, Jiache Zhang +5
Large numbers of synthesized videos from diffusion models pose threats to information security and authenticity, leading to an increasing demand for generated content detection. Ho…
Towards Effective User Attribution for Latent Diffusion Models via Watermark-Informed Blending
Yongyang Pan, Xiaohong Liu, Siqi Luo +5
Rapid advancements in multimodal large language models have enabled the creation of hyper-realistic images from textual descriptions. However, these advancements also raise signifi…
Hierarchical Fine-Grained Image Forgery Detection and Localization
Xiao Guo, Xiaohong Liu, Zhiyuan Ren +3
Differences in forgery attributes of images generated in CNN-synthesized and image-editing domains are large, and such differences make a unified image forgery detection and locali…