← Back to all news
arXiv Computer Vision By Zeyu Ye, Yanchun Li, Sibei He, Meng Xie, Hangtao Zhang, Xianlong Wang, Li Zeng, Jiahao Chen, Yichen Wang, Junhui Wang, Ziqi Zhou

False Claims, Credible Images: A Red-Teaming Benchmark for Commercial Image Generators

Read the original on arXiv Computer Vision →

The paper introduces EpiReal-Bench, a benchmark of 10,000 false-claim prompts and corresponding images designed to test visual misinformation risks in commercial image generators. It also presents EpiReal-Attack, a black-box optimization framework that can bypass alignment safeguards, increasing the success rate of generating misleading images from 70% to 95%. Experiments on four commercial models show that these generators can produce credible visual evidence for false claims, highlighting a blind spot in current alignment methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv AI
Sep 28

What Improves Multimodal Misinformation Detection? Answers from a Large-Scale Empirical Study

The paper investigates how different multimodal design choices affect the performance of misinformation detection systems. Using over 3,375 experiments across three benchmark datasets and various pre‑trained vision and language models, the authors systematically compare design options and conduct robustness analyses. The study offers practical guidance on which choices improve detection, when they may fail silently, and which pipeline components most influence model behavior, addressing four key research questions.

By Akshit Sharma, Prashant W. Patil
multimodalbenchmarks
More like this →