collaborators

14 papers

cs.CV2026

LoRC: Detecting AI-Generated Images via Low-Rank Collapse in Semantic Residuals

Haozhen Yan, Ruoxin Chen, Jiahui Zhan +6

Modern generators faithfully model macroscopic semantics, producing synthetic images that appear highly realistic. Consequently, decisive forensic cues reside in subtle non-semanti…

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

GenShield: Unified Detection and Artifact Correction for AI-Generated Images

Zhipei Xu, Xuanyu Zhang, Youmin Xu +5

Diffusion-based image synthesis has made AI-generated images (AIGI) increasingly photorealistic, raising urgent concerns about authenticity in applications such as misinformation d…

cs.CV2026

VRAG-DFD: Verifiable Retrieval-Augmentation for MLLM-based Deepfake Detection

Hui Han, Shunli Wang, Yandan Zhao +2

In Deepfake Detection (DFD) tasks, researchers proposed two types of MLLM-based methods: complementary combination with small DFD detectors, or static forgery knowledge injection.…

cs.CV2026

All Patches Matter, More Patches Better: Enhance AI-Generated Image Detection via Panoptic Patch Learning

Zheng Yang, Ruoxin Chen, Zhiyuan Yan +8

The exponential growth of AI-generated images (AIGIs) underscores the urgent need for robust and generalizable detection methods. In this paper, we establish two key principles for…

cs.CV2026

ForgeryVCR: Visual-Centric Reasoning via Efficient Forensic Tools in MLLMs for Image Forgery Detection and Localization

Youqi Wang, Shen Chen, Haowei Wang +6

Existing Multimodal Large Language Models (MLLMs) for image forgery detection and localization predominantly operate under a text-centric Chain-of-Thought (CoT) paradigm. However,…