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cs.CV2026

MotionPhys: Detecting AI-Generated Videos via Physical Consistency of Optical-Flow Trajectories

Haojin He, Hao Tan, Zichang Tan +2

Modern AI video generation models can produce videos with high visual fidelity and seemingly smooth temporal transitions. However, visual realism does not necessarily imply physica…

cs.CV2026

Unleashing the Potential of Vision-Language Models for Generalizable AI-Generated Image Detection

Weihan Cai, Hao Tan, Zichang Tan +2

Recent work has shown that a simple linear probe on frozen representations from modern vision foundation models (VFMs) can achieve state-of-the-art AIGI detection performance, subs…

cs.CV2026

Veritas++: Value-aware On-Policy Distillation for Perception-Enhanced AIGI Detection

Hao Tan, Jun Lan, Zichang Tan +7

Veritas++ introduces a perception‑enhanced framework for detecting AI‑generated images by training models to capture fine‑grained visual details, semantic anomalies, and pixel‑leve…

cs.CV2026

HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection

Senyuan Shi, Hao Tan, Zichang Tan +4

The rapid evolution of generative models has precipitated a proliferation of fabricated content, posing significant challenges to existing Synthetic Image Detection (SID) methods.…

cs.CV2026

ForensicZip: More Tokens are Better but Not Necessary in Forensic Vision-Language Models

Yingxin Lai, Zitong Yu, Jun Wang +3

Multimodal Large Language Models (MLLMs) enable interpretable multimedia forensics by generating textual rationales for forgery detection. However, processing dense visual sequence…

cs.CV2026

Veritas: Generalizable Deepfake Detection via Pattern-Aware Reasoning

Hao Tan, Jun Lan, Zichang Tan +7

Deepfake detection remains a formidable challenge due to the complex and evolving nature of fake content in real-world scenarios. However, existing academic benchmarks suffer from…