This technique strives to make AI models more robust to adversarial inputs via adversarial training or network distillation. Examples include (i) using randomization to inject noise during training to enhance resilience to evasion attacks (especially triggered by subtle perturbations), (ii) Gradient Masking, (iii) Feature Squeezing.

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Explanation

 

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How it works

 

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How to implement