# ML Attack Models: Adversarial Attacks and Data Poisoning Attacks Source: [ML Attack Models: Adversarial Attacks and Data Poisoning Attacks]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:46:07.580Z Authors: Jing Lin, Long Dang, Mohamed Rahouti, Kaiqi Xiong Published: 2021-12-06T05:59:30.000Z ## Abstract Many state-of-the-art ML models have outperformed humans in various tasks such as image classification. With such outstanding performance, ML models are widely used today. However, the existence of adversarial attacks and data poisoning attacks really questions the robustness of ML models. For instance, Engstrom et al. demonstrated that state-of-the-art image classifiers could be easily fooled by a small rotation on an arbitrary image. As ML systems are being increasingly integrated into safety and security-sensitive applications, adversarial attacks and data poisoning attacks pose a considerable threat. This chapter focuses on the two broad and important areas of ML security: adversarial attacks and data poisoning attacks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()