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20242026
most citedDetecting AI-Generated Video via Frame Consistency

1 citations · 1 across the 7 of their papers we have counts for

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9 papers

cs.CV2026

Deep Residual Injection for Full-Spectrum Forensic Signal Perception in Multimodal Large Language Models

Kaiqing Lin, Zhiyuan Yan, Ruoxin Chen +8

Multimodal large language models (MLLMs) have been increasingly adopted in forensics for their robust semantic understanding. As AI-generated images become realistic, semantic-leve…

cs.CV2026

Disentangling Hallucinations: Orthogonal Semantic Projection for Robust Interpretability

Emirhan Bilgiç, Baptiste Caramiaux, Zhi Yan +1

As Vision-Language Models are increasingly deployed in safety-critical applications, the trustworthiness of their explanations becomes crucial. Explainable AI (XAI) methods for Vis…

cs.CV2026

FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection

Leqi Zhu, Junyan Ye, Kaiqing Lin +3

The development of generative artificial intelligence technologies has propelled the visual realism of synthetic images to an unprecedented level. Although current interpretable de…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

Your One-Stop Solution for AI-Generated Video Detection

Long Ma, Zihao Xue, Yan Wang +6

Recent advances in generative modeling can create remarkably realistic synthetic videos, making it increasingly difficult for humans to distinguish them from real ones and necessit…