2 citations · 3 across the 9 of their papers we have counts for
10 papers
MarkCleaner: High-Fidelity Watermark Removal via Imperceptible Micro-Geometric Perturbation
Xiaoxi Kong, Jieyu Yuan, Pengdi Chen +3
Semantic watermarks exhibit strong robustness against conventional image-space attacks. In this work, we show that such robustness does not survive under micro-geometric perturbati…
MPF-Net: Exposing High-Fidelity AI-Generated Video Forgeries via Hierarchical Manifold Deviation and Micro-Temporal Fluctuations
Xinan He, Kaiqing Lin, Yue Zhou +8
With the rapid advancement of video generation models such as Veo and Wan, the visual quality of synthetic content has reached a level where macro-level semantic errors and tempora…
ForgeryVCR: Visual-Centric Reasoning via Efficient Forensic Tools in MLLMs for Image Forgery Detection and Localization
Youqi Wang, Shen Chen, Haowei Wang +6
Existing Multimodal Large Language Models (MLLMs) for image forgery detection and localization predominantly operate under a text-centric Chain-of-Thought (CoT) paradigm. However,…
Simplicity Prevails: The Emergence of Generalizable AIGI Detection in Visual Foundation Models
Yue Zhou, Xinan He, Kaiqing Lin +3
While specialized detectors for AI-Generated Images (AIGI) achieve near-perfect accuracy on curated benchmarks, they suffer from a dramatic performance collapse in realistic, in-th…
Brought a Gun to a Knife Fight: Modern VFM Baselines Outgun Specialized Detectors on In-the-Wild AI Image Detection
Yue Zhou, Xinan He, Kaiqing Lin +4
While specialized detectors for AI-generated images excel on curated benchmarks, they fail catastrophically in real-world scenarios, as evidenced by their critically high false-neg…
Semantic Visual Anomaly Detection and Reasoning in AI-Generated Images
Chuangchuang Tan, Xiang Ming, Jinglu Wang +5
The rapid advancement of AI-generated content (AIGC) has enabled the synthesis of visually convincing images; however, many such outputs exhibit subtle \textbf{semantic anomalies},…
Webly-Supervised Image Manipulation Localization via Category-Aware Auto-Annotation
Chenfan Qu, Yiwu Zhong, Huiguo He +2
Images manipulated by image editing tools can mislead viewers and pose significant risks to social security. However, accurately localizing manipulated image regions remains challe…
Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection
Yue Zhou, Xinan He, KaiQing Lin +3
Current AIGC detectors often achieve near-perfect accuracy on images produced by the same generator used for training but struggle to generalize to outputs from unseen generators.…
VLForgery Face Triad: Detection, Localization and Attribution via Multimodal Large Language Models
Xinan He, Yue Zhou, Bing Fan +3
Faces synthesized by diffusion models (DMs) with high-quality and controllable attributes pose a significant challenge for Deepfake detection. Most state-of-the-art detectors only…
Redundant Semantic Environment Filling via Misleading-Learning for Fair Deepfake Detection
Xinan He, Yue Zhou, Shu Hu +3
Detecting falsified faces generated by Deepfake technology is essential for safeguarding trust in digital communication and protecting individuals. However, current detectors often…