Adversarial Robustness Explained
Robustness in machine learning refers to a model's ability to maintain accurate outputs despite small, potentially adversarial perturbations to input data. Daniel discusses how traditional defenses have focused on specific types of attacks, primarily LP attacks, while introducing novel attack vectors that exploit JPEG compression and other methods. These new approaches, including elastic and weather-based attacks, broaden the understanding of adversarial threats beyond conventional perturbations.In this clip
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Data Skeptic
Robustness to Unforeseen Adversarial Attacks
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