InfoResearchPreprint
WildFakeToolBench: Benchmarking AI-Generated Image Detectors with Web-Sourced Image Processing
- Published
- Record updated
Summary
The authors introduce WildFakeToolBench, a benchmark of 156 executable image-processing tools derived from 220 public web sources across 142 domains, built to test whether such tools can help evade AI-generated image detectors. On 105 images from five generators, the strongest individual tools reach attack success rates of 25.25% to 100% across six detectors, and eight-tool subsets reach worst-case coverage above 80% for every detector. The authors note that effectiveness is sparse and detector-specific, so broad coverage does not imply reliable one-shot evasion.
Related items
- InfoMirage: Targeted Jailbreaks Against Post-Generation Safety Checkers in Multimodal Diffusion ModelsSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
- InfoWhere AI Vulnerability Discovery Harnesses Go Quiet: Structural False Negatives and Hidden VulnerabilitiesSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
- InfoA Unified Evaluation Framework of Physical Adversarial Robustness in the Edge VLM EraSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
- InfoCorrelated Failures of AI Approval MonitorsSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
- InfoRobust Fine‐Tuning Based on Fuzz Testing for Chinese BERT‐Based PLMsSimilar attack · OpenAlex (peer-reviewed AI security)