Mingjie Li, Yukun Jiang, Yicong Tan, Yang Zhang · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.31152 · cited by 0 works (OpenAlex) · open-access PDF
وبالرغم من أن الهواتف الذكية التي تم إنشاؤها من قبل المستخدمين المحتملين أصبحت أكثر شعبية في هذه الأيام، فإن المحادثات حول هذه التقنيات موجودة على نطاق واسع في المجلات العامة، والمجلات البرمجية، والمجلات الفوتوغرافية. بالإضافة إلى ذلك، مع المساعدة في ترجمة الكود المنفصل عن LLM، يمكن للمستخدمين الخبيثين استخدام هذه المعلومات لتجنب الهواتف الذكية دون المعرفة المتخصصة أو إجراء GPU المحلي من الأدوات الناجحة. لتقييم هذه المخاطر، نحن نقدم \textsc{WildFakeToolBench}، مجموعة مقارنة من أدوات معالجة الصور المنفصلة عن 156 دولار أمريكيًا التي تم إنشاؤها من مصادر عامة تتراوح
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 and passes syntax, execution, and output-distortion checks without using feedback from the evaluated detectors. On $105$ generated images from five generators, we evaluate six state-of-the-art detectors with images processed by the tools in our collected bench to assess their effectiveness against such in-the-wild risks. The strongest individual tools achieve attack success rates of $25.25\%$--$100\%$ across detectors, exceeding the strongest of six selected image-processing baselines for each detector. Evaluating subsets of an eight-tool pool yields worst-case coverage above $80\%$ for every detector. Effectiveness is sparse and detector-specific: high coverage across candidates does not imply reliable one-shot evasion. These findings not only highlight the potential of such web-sourced risks, but also show the potential on establishing AI-image detector robustness evaluation framework based on those in-the-wild information.
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Topics: Benchmarking, Suite, Computer science, Executable, Detector, Robustness (evolution)
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