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DTA: Distribution Transform-based Attack for Query-Limited Scenario
In generating adversarial examples, the conventional black-box attack methods rely on sufficient feedback from the to-be-attacked models by repeatedly querying until the attack is successful, which usually results in thousands of trials during an attack.
AFLOW: Developing Adversarial Examples under Extremely Noise-limited Settings
Extensive studies have demonstrated that deep neural networks (DNNs) are vulnerable to adversarial attacks.
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