Mingjie Li, Yukun Jiang, Yicong Tan, Yang Zhang · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31152 · open-access PDF
随着人工智能生成的图像日益流行,在当前的公共论坛、软件存储库和摄影教程中广泛存在关于这些世代的讨论,此外,借助LLM辅助的代码翻译,恶意行为者可以采用此类信息来逃避探测器,而无需专业知识或本地GPU执行所产生的工具。
Machine translation (Meta M2M100); the English original follows.
As AI generated images are getting popular these days, discussions on such generations are widely existed in current public forums, software repositories, and photography tutorials. Moreover, with the help of LLM-assisted code translation, malicious actors can adopt such information to evade detectors without specialized knowledge or local GPU execution of the resulting tools. To evaluate such risks, we introduce \textsc{WildFakeToolBench}, a benchmark suite of $156$ executable image-processing tools derived from $220$ public sources across $142$ domains. Each tool retains its source evidence
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