collaborators

7 papers

cs.CV2026

EviScene: Beyond "Is It AI?" for Agentic Image Factuality Assessment

ZiAn Wang, Ruiqi Liu, Zhengbo Zhang +10

Determining whether an image is AI-generated does not establish whether the situation it depicts is factually supported. We formulate Image Factuality Assessment: given an image an…

cs.AI2026

Exploring and Bridging Knowledge Holes in Unlearned Multimodal Large Language Models

Junxiang You, Junkai Chen, Yuhao He +3

Machine unlearning offers a promising approach to remove unsafe content from Multimodal Large Language Models (MLLMs), yet ensuring the precision of unlearning remains a persistent…

cs.CV2026

GlobalForge: Towards Robust AI-Generated Image Detection

Manni Cui, Ruiqi Liu, Dianyuan Zou +8

AI-generated image (AIGI) detectors achieve strong accuracy on clean benchmarks, but their performance drops sharply after images are propagated through real-world channels. We tra…

cs.CV2026

Visual-Noise Guided In-Context Distillation for Multimodal Large Language Model Unlearning

Junkai Chen, Yuhao He, Junxiang You +3

Multimodal Large Language Models (MLLMs) have achieved remarkable progress on vision-language tasks, but they may also memorize and expose sensitive or restricted knowledge, raisin…

cs.LG2026

Explanation-Guided Adversarial Training for Robust and Interpretable Models

Chao Chen, Yanhui Chen, Shanshan Lin +4

Deep neural networks (DNNs) have achieved remarkable performance in many tasks, yet they often behave as opaque black boxes. Explanation-guided learning (EGL) methods steer DNNs us…

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.CV2025

Beyond Artifacts: Real-Centric Envelope Modeling for Reliable AI-Generated Image Detection

Ruiqi Liu, Yi Han, Zhengbo Zhang +9

The rapid progress of generative models has intensified the need for reliable and robust detection under real-world conditions. However, existing detectors often overfit to generat…