Adversarial Examples on Object Recognition

By A. Serban, Erik Poll, Joost Visser. 2020

85 citations recorded by the source corpus.

Deep neural networks are at the forefront of machine learning research. However, despite achieving impressive performance on complex tasks, they can be very sensitive: Small perturbations of inputs can be sufficient to induce incorrect behavior. Such perturbations, called adversarial examples, are intentionally designed to test the network’s sensitivity to distribution drifts. Given their surprisingly small size, a wide body of literature conjectures on their existence and how this phenomenon can be mitigated. In this article, we discuss the impact of adversarial examples on security, safety, and robustness of neural networks. We start by introducing the hypotheses behind their existence, the methods used to construct or protect against them, and the capacity to transfer adversarial examples between different machine learning models. Altogether, the goal is to provide a comprehensive and self-contained survey of this growing field of research.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition (2020, background): “Moreover, where humans are tolerant of moderate changes in object position, size, pose, contrast, illumination and clutter [14], deep image classification models might not be robust for such transformations [40, 44].”
Membership Inference Attacks on Machine Learning: A Survey (2021, background): “There are many surveys that summarize different attacks on ML models [35, 84, 114, 117, 137, 155, 166, 168, 176, 190, 228].”