Security

Are adversarial examples inevitable?

in machine learning, adversarial learning

2018-09-05

A number of adversarial attacks on neural networks have been recently proposed. To counter these attacks, a number of authors have proposed a range of defenses. However, these defenses are often quickly broken by new and revised attacks. Given the lack of success at generating robust defenses, we are led to ask a fundamental question: Are adversarial attacks inevitable?

We identify a broad class of problems for which adversarial examples cannot be avoided. We also derive fundamental limits on the susceptibility of a classifier to adversarial attacks that depend on properties of the data distribution as well as the dimensionality of the dataset.

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Poison Frogs! Targeted Poisoning Attacks on Neural Networks

in adversarial learning, machine learning

2018-04-11

What are poisoning attacks?

Before deep learning algorithms can be deployed in security-critical applications, their robustness against adversarial attacks must be put to the test. The existence of adversarial examples in deep neural networks (DNNs) has triggered debates on how secure these classifiers are. Adversarial examples fall within a category of attacks called evasion attacks. Evasion attacks happen at test time – a clean target instance is modified to avoid detection by a classifier, or spur misclassification.

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