works on

From the 1 of 8 linked papers with an AI index.

most citedStegaFFD: Privacy-Preserving Face Forgery Detection via Fine-Grained Steganographic Domain Lifting

3 citations · 3 across the 6 of their papers we have counts for

collaborators

8 papers

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

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

CPG-PAD: Concept-Informed Prompts Guided Presentation Attack Detection

Haoyuan Zhang, Xiangyu Zhu, Li Gao +3

Presentation Attack Detection (PAD) serves as a crucial safeguard for face recognition systems against presentation attacks such as printed photos, replayed videos, and 3D masks. D…

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

AGC: Adaptive Geodesic Correction for Adversarial Robustness on Vision-Language Models

Zhiwei Li, Jiacheng Xue, Weining Wang +4

Vision-language models like CLIP have demonstrated remarkable zero-shot transfer capabilities. However, their susceptibility to imperceptible adversarial perturbations remains a cr…

cs.CV2026

From Intuition to Investigation: A Tool-Augmented Reasoning MLLM Framework for Generalizable Face Anti-Spoofing

Haoyuan Zhang, Keyao Wang, Guosheng Zhang +11

Face recognition remains vulnerable to presentation attacks, calling for robust Face Anti-Spoofing (FAS) solutions. Recent MLLM-based FAS methods reformulate the binary classificat…