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This paper introduces AESOP, a framework for adversarial execution-path selection that significantly inflates FLOPs and latency in deep learning inference pipelines, revealing new efficiency-based vulnerabilities.
This article examines adversarial attacks on machine learning models and demonstrates why gradient masking—a defensive technique that attempts to deny attackers access to useful gradients—is fundamentally ineffective. The paper shows that attackers can circumvent gradient masking by training substitute models that mimic the defended model's behavior, making the defense strategy ultimately futile.