How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses

Understand

Deep Learning is currently used to perform multiple tasks, such as object recognition, face recognition, and natural language processing.

• However, Deep Neural Networks (DNNs) are vulnerable to perturbations that alter the network prediction (adversarial examples), raising concerns regarding its usage in critical areas, such as self-driving vehicles, malware detection, and healthcare.
• This paper compiles the most recent adversarial attacks, grouped by the attacker capacity, and modern defenses clustered by protection strategies.
• We also present the new advances regarding Vision Transformers, summarize the datasets and metrics used in the context of adversarial settings, and compare the state-of-the-art results under different attacks, finishing with the identification of open issues.