The Taboo Trap: Behavioural Detection of Adversarial Samples

Understand

Deep Neural Networks (DNNs) have become a powerful toolfor a wide range of problems.

• Yet recent work has found an increasing variety of adversarial samplesthat can fool them.
• Most existing detection mechanisms against adversarial attacksimpose significant costs, either by using additional classifiers to spot adversarial samples, or by requiring the DNN to be restructured.
• In this paper, we introduce a novel defence.