Defense mechanism and adversarical attacks A Review

As AI as well as DL approaches rapidly advance, it is more important than ever to guarantee the safety and reliability of the implemented methods. In recent years, it has become common knowledge that DL algorithms are susceptible to security breaches when presented with hostile data. Even while people may not notice any wrongdoing, the DL models may act in unexpected ways due to the faked samples. Adversarial assaults have been successfully implemented in real-world, physical circumstances, adding more evidence to their viability. As a result, adversarial attack and response approaches have emerged as a focus of intense study in the fields of machine learning and cybersecurity in recent years. First, we layout the theoretical foundations, algorithmic mechanisms, and practical uses for adversarial attack approaches in this study. Then, we detail a few ongoing research projects on defensive tactics that span the field's vast frontier. Subsequently, we examine a number of outstanding issues and difficulties, with the intention of stimulating more study into this vital subj ect.

• Publication year

  2022

• Venue

  2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC)

• Publication date

  2022-11-18

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/IIHC55949.2022.10060164
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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