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cs.CV2026

No Way To Steal My Face: Proactive Defense Against Identity-Preserving Personalized Generation

Lizhi Xiong, Jun Li, Ziqiang Li +2

Recent advances in diffusion models have enabled high-fidelity, identity-preserving image generation for personalized applications such as digital avatars and virtual try-on system…

cs.CV2026

Beyond Text Prompts: Precise Concept Erasure through Text-Image Collaboration

Jun Li, Lizhi Xiong, Ziqiang Li +4

Text-to-image generative models have achieved impressive fidelity and diversity, but can inadvertently produce unsafe or undesirable content due to implicit biases embedded in larg…

cs.CV2026

PhysInOne: Visual Physics Learning and Reasoning in One Suite

Siyuan Zhou, Hejun Wang, Hu Cheng +36

We present PhysInOne, a large-scale synthetic dataset addressing the critical scarcity of physically-grounded training data for AI systems. Unlike existing datasets limited to mere…

cs.CV2026

Generalizable Deepfake Detection Based on Forgery-aware Layer Masking and Multi-artifact Subspace Decomposition

Xiang Zhang, Wenliang Weng, Daoyong Fu +4

Deepfake detection remains highly challenging, particularly in cross-dataset scenarios and complex real-world settings. This challenge mainly arises because artifact patterns vary…

cs.CV2025

How Noise Benefits AI-generated Image Detection

Ziqiang Li, Jiazhen Yan, Fan Wang +2

The rapid advancement of generative models has made real and synthetic images increasingly indistinguishable. Although extensive efforts have been devoted to detecting AI-generated…

cs.CV2025

DGS-Net: Distillation-Guided Gradient Surgery for CLIP Fine-Tuning in AI-Generated Image Detection

Jiazhen Yan, Ziqiang Li, Fan Wang +3

The rapid progress of generative models such as GANs and diffusion models has led to the widespread proliferation of AI-generated images, raising concerns about misinformation, pri…

cs.CV2025

NS-Net: Decoupling CLIP Semantic Information through NULL-Space for Generalizable AI-Generated Image Detection

Jiazhen Yan, Fan Wang, Weiwei Jiang +3

The rapid progress of generative models, such as GANs and diffusion models, has facilitated the creation of highly realistic images, raising growing concerns over their misuse in s…

cs.CV2025

Is Artificial Intelligence Generated Image Detection a Solved Problem?

Ziqiang Li, Jiazhen Yan, Ziwen He +4

The rapid advancement of generative models, such as GANs and Diffusion models, has enabled the creation of highly realistic synthetic images, raising serious concerns about misinfo…

cs.CV2025

A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models

Ziqiang Li, Jun Li, Lizhi Xiong +2

Text-to-image diffusion models have made significant advancements in generating high-quality, diverse images from text prompts. However, the inherent limitations of textual signals…

cs.CV2025

Dual Frequency Branch Framework with Reconstructed Sliding Windows Attention for AI-Generated Image Detection

Jiazhen Yan, Ziqiang Li, Fan Wang +2

The rapid advancement of Generative Adversarial Networks (GANs) and diffusion models has enabled the creation of highly realistic synthetic images, presenting significant societal…