Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples

Understand

Adversarial sample attacks perturb benign inputs to induce DNN misbehaviors.

• Recent research has demonstrated the widespread presence and the devastating consequences of such attacks.
• Existing defense techniques either assume prior knowledge of specific attacks or may not work well on complex models due to their underlying assumptions.
• We argue that adversarial sample attacks are deeply entangled with interpretability of DNN models: while classification results on benign inputs can be reasoned based on the human perceptible features/attributes, results on adversarial samples can hardly be explained.