Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty

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Abstract:

Over the past decade, deep learning (DL) has been successfully applied to many industrial domain-specific tasks. However, the current state-of-the-art DL software still suffers from quality issues, which raises great concern especially in the context of safety- and security-critical scenarios. Adversarial examples (AEs) represent a typi...More

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