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cs.CV2026

EvoGuard: An Extensible Agentic RL-based Framework for Practical and Evolving AI-Generated Image Detection

Chenyang Zhu, Maorong Wang, Jun Liu +2

The rapid proliferation of AI-Generated Images (AIGIs) poses severe misinformation risks, making AIGI detection critical yet challenging. Traditional detection paradigms mainly rel…

cs.CV2026

Rethinking Brain Decoding with CLIP: The Role of Adversarial Robustness

Byeongseo Bok, Futa Waseda, Jun Liu +1

Brain decoding aims to uncover neural mechanisms by inferring stimulus-related representations from brain signals. In fMRI studies, this is typically achieved by mapping fMRI respo…

cs.CV2026

Forged Calamity: Benchmark for Cross-Domain Synthetic Disaster Detection in the Age of Diffusion

Duc-Manh Phan, Quoc-Duy Tran, Duy-Khang Do +9

The rapid advancement of text-to-image diffusion models has enabled the creation of highly photorealistic synthetic images that closely resemble real photographs, making it increas…

cs.CV2025

Quality Text, Robust Vision: The Role of Language in Enhancing Visual Robustness of Vision-Language Models

Futa Waseda, Saku Sugawara, Isao Echizen

Defending pre-trained vision-language models (VLMs), such as CLIP, against adversarial attacks is crucial, as these models are widely used in diverse zero-shot tasks, including ima…

cs.CV2025

DRAGON: A Large-Scale Dataset of Realistic Images Generated by Diffusion Models

Giulia Bertazzini, Daniele Baracchi, Dasara Shullani +2

The remarkable ease of use of diffusion models for image generation has led to a proliferation of synthetic content online. While these models are often employed for legitimate pur…