The Vulnerability of Learning to Adversarial Perturbation Increases with Intrinsic Dimensionality

Abstract

Recent research has shown that machine learning systems, including state-of-the-art deep neural networks, are vulnerable to adversarial attacks. By adding to the input object an imperceptible amount of adversarial noise, it is highly likely that the classifier can be tricked into assigning the modified object to any desired class. Furthermore, these adversarial samples generalize well across models: samples generated using one network can often succeed in fooling other networks or machine learning models. These alarming properties of adversarial samples have drawn increasing interest recently, with several researchers having attributed the adversarial effect to different factors, such as the..