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Defending Against Adversarial Attacks in Artificial Intelligence Technologies

Year: 2025

Type: article

Abstract: The rapid adoption of artificial intelligence (AI) technologies across diverse sectors has exposed vulnerabilities, particularly to adversarial attacks designed to deceive AI models by manipulating input data. This paper comprehensively reviews adversarial attacks, categorising them into training-phase and testing-phase types, with testing-phase attacks further divided into white-box and black-box categories. We explore defence mechanisms such as data modification, model enhancement, and auxiliary tools, focusing on the critical need for robust AI security in healthcare and autonomous systems sectors. Additionally, the paper highlights the role of AI in cybersecurity, offering a taxonomy for AI applications in threat detection, vulnerability assessment, and incident response. By analysing current defence strategies and outlining potential research directions, this paper aims to enhance the resilience of AI systems against adversarial threats, thereby strengthening AI's deployment in sensitive applications.

Source: International Journal of Information and Communication Technology Research

DOI: 10.66224/itrc.17.2.9

Authors: Seyed Hadi Sajjadi, Sasan Karamizadeh, Amirmansour Yadegari

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7128323663

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