Evaluating the Vulnerabilities in ML systems in terms of adversarial attacks

• URL: http://arxiv.org/abs/2308.12918v1
• Date: Thu, 24 Aug 2023 16:46:01 GMT
• Title: Evaluating the Vulnerabilities in ML systems in terms of adversarial attacks
• Authors: John Harshith, Mantej Singh Gill, Madhan Jothimani
• Abstract summary: New adversarial attacks methods may pose challenges to current deep learning cyber defense systems. Authors explore the consequences of vulnerabilities in AI systems. It is important to train the AI systems appropriately when they are in testing phase and getting them ready for broader use.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There have been recent adversarial attacks that are difficult to find. These new adversarial attacks methods may pose challenges to current deep learning cyber defense systems and could influence the future defense of cyberattacks. The authors focus on this domain in this research paper. They explore the consequences of vulnerabilities in AI systems. This includes discussing how they might arise, differences between randomized and adversarial examples and also potential ethical implications of vulnerabilities. Moreover, it is important to train the AI systems appropriately when they are in testing phase and getting them ready for broader use.

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