A Comparative Analysis of Adversarial Attack Methods on Machine Learning Models

The broad adoption of machine learning (ML) models in many applications has sparked worries about their susceptibility to adversarial attacks, in which slight alterations to input data result in inaccurate model predictions. This study does a comparative examination of adversarial attack techniques on machine learning models, assessing their efficacy, complexity, and current mitigation measures. The analysis explores several attack methodologies, such as gradient-based, decision-based, and optimization-based methods. Each method exploits different flaws in machine learning models to create adversarial instances. An assessment is conducted to determine the vulnerability of different ML model designs, such as neural networks, support vector machines, and decision trees, to manipulation in light of these assaults. Additionally, the study investigates Défense measures, such as adversarial training, input pre- processing, and model robustness verification, that are designed to reduce the effects of adversarial attacks and improve the resilience of the model. This comparative research offers valuable insights into the changing environment of adversarial attacks on machine learning models, emphasizing the importance of implementing strong Défense mechanisms to protect against possible threats. This research aims to enhance the security and dependability of machine learning systems against hostile manipulation, hence promoting trust and confidence in their practical implementation. Key Words: MNIST, artificial intelligence, dataset adversarial Attacks, Machine Learning, Adversarial Examples, robustness, Fast Gradient Sign Method, DeepFool, Carlini & Wagner (C&W), Zoo-Adversarial Instance Optimization,