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Output Randomization: A Novel Defense for both White-box and Black-box Adversarial Models
Adversarial examples pose a threat to deep neural network models in a variety of scenarios, from settings where the adversary has complete knowledge of the model in a "white box" setting and to the opposite in a "black box" setting.
Thwarting finite difference adversarial attacks with output randomization
Adversarial examples pose a threat to deep neural network models in a variety of scenarios, from settings where the adversary has complete knowledge of the model and to the opposite "black box" setting.
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