AI Research
Adversarial Risk in the Generative AI Era Necessitates Dropping the Small Epsilon Ball
A case for broadening adversarial machine-learning research beyond imperceptible perturbations
Adversarial Machine Learning research is currently centered on the search for the epsilon-ball: minimal perturbations designed to fool models while remaining imperceptible to humans.
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ArXiv CS.LG
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ArXiv CS.LG
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Generated insight by OnAirToday AI
Focusing on tiny, imperceptible input changes may leave adversarial risks in generative AI underexamined. Broadening the scope of research could better reflect the risks the article highlights.
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