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Adversarial Machine Learning: Attacks, Defences, and the Road Ahead

Year: 2025

Type: article

Abstract: Adversarial Machine Learning (AML) represents a growing field of study focusing on the vulnerabilities of machine learning models to maliciously crafted inputs. This paper provides a comprehensive overview of AML, including attack types, defence mechanisms, and a detailed evaluation framework. By combining simulated experiments with real-world datasets, expert interviews, and comparative benchmarking, we investigate the performance, robustness, and feasibility of various adversarial strategies and countermeasures. Our findings reveal critical trade-offs between model accuracy and robustness, suggesting a need for adaptive, context-aware defences in AI systems.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15354151

Author: Ghritlahare, Akhilesh

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6912057527

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