A survey of practical adversarial example attacks

• Main Authors: Lu Sun, Mingtian Tan, Zhe Zhou
• Format: Article
• Language: English
• Published: SpringerOpen 2018-09-01
• Series: Cybersecurity
• Subjects: AI systems security; Adversarial examples; Attacks
• Online Access: http://link.springer.com/article/10.1186/s42400-018-0012-9

Abstract Adversarial examples revealed the weakness of machine learning techniques in terms of robustness, which moreover inspired adversaries to make use of the weakness to attack systems employing machine learning. Existing researches covered the methodologies of adversarial example generation, the root reason of the existence of adversarial examples, and some defense schemes. However practical attack against real world systems did not appear until recent, mainly because of the difficulty in injecting a artificially generated example into the model behind the hosting system without breaking the integrity. Recent case study works against face recognition systems and road sign recognition systems finally abridged the gap between theoretical adversarial example generation methodologies and practical attack schemes against real systems. To guide future research in defending adversarial examples in the real world, we formalize the threat model for practical attacks with adversarial examples, and also analyze the restrictions and key procedures for launching real world adversarial example attacks.