Adversarial Attacks: Key Challenges for Security Defense in the Age of Intelligence

The development of Artificial Intelligence (AI) technology has revolutionized various industries. However, the vulnerability of deep learning models, which are the core technology of AI, has been gradually exposed, among which adversarial attacks have become a prominent security issue. Adversarial attacks pose a threat to the reliability of AI systems by adding tiny perturbations to the input data to mislead the model and lead to wrong prediction results. This paper offers an exhaustive overview of adversarial attacks, encompassing their definitions, taxonomies, and the methodologies for crafting adversarial examples. A comparative analysis of the strengths and weaknesses inherent to different adversarial examples generation techniques is presented, alongside an exposition of their critical influence on the security landscape of deep learning technologies. Finally, the discourse concludes with a prospective outlook on the evolving trends in adversarial attacks.