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cs.CV2026

ForensicConcept: Transferable Forensic Concepts for AIGI Detection

Menyanshu Zhou, Ziyin Zhou, Ke Sun +4

AI-generated image detectors achieve high accuracy on in-distribution data but often fail on unseen generators. A key obstacle to understanding this failure is the black-box nature…

cs.CV2025

M4-BLIP: Advancing Multi-Modal Media Manipulation Detection through Face-Enhanced Local Analysis

Hang Wu, Ke Sun, Jiayi Ji +2

In the contemporary digital landscape, multi-modal media manipulation has emerged as a significant societal threat, impacting the reliability and integrity of information dissemina…

cs.CV2025

Exploring the Collaborative Advantage of Low-level Information on Generalizable AI-Generated Image Detection

Ziyin Zhou, Ke Sun, Zhongxi Chen +5

Existing state-of-the-art AI-Generated image detection methods mostly consider extracting low-level information from RGB images to help improve the generalization of AI-Generated i…

cs.CV2025

AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Models

Ziyin Zhou, Yunpeng Luo, Yuanchen Wu +7

The rapid development of AI-generated content (AIGC) technology has led to the misuse of highly realistic AI-generated images (AIGI) in spreading misinformation, posing a threat to…

cs.CV2024

DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion

Ke Sun, Shen Chen, Taiping Yao +4

The rapid progress of Deepfake technology has made face swapping highly realistic, raising concerns about the malicious use of fabricated facial content. Existing methods often str…

cs.CV2024

StealthDiffusion: Towards Evading Diffusion Forensic Detection through Diffusion Model

Ziyin Zhou, Ke Sun, Zhongxi Chen +3

The rapid progress in generative models has given rise to the critical task of AI-Generated Content Stealth (AIGC-S), which aims to create AI-generated images that can evade both f…