2 citations · 2 across the 8 of their papers we have counts for
12 papers · 1 filter
Towards Policy-Adaptive Image Guardrail: Benchmark and Method
Caiyong Piao, Zhiyuan Yan, Haoming Xu +4
Accurate rejection of sensitive or harmful visual content, i.e., harmful image guardrail, is critical in many application scenarios. This task must continuously adapt to the evolvi…
MIRROR: Manifold Ideal Reference ReconstructOR for Generalizable AI-Generated Image Detection
Ruiqi Liu, Manni Cui, Ziheng Qin +12
High-fidelity generative models have narrowed the perceptual gap between synthetic and real images, posing serious threats to media security. Most existing AI-generated image (AIGI…
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…
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…
AlignGemini: Generalizable AI-Generated Image Detection Through Task-Model Alignment
Ruoxin Chen, Jiahui Gao, Kaiqing Lin +5
Vision Language Models (VLMs) are increasingly used for detecting AI-generated images (AIGI). However, converting VLMs into reliable detectors is resource-intensive, and the result…
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…