Simple Black-Box Adversarial Perturbations for Deep Networks

Understand

Deep neural networks are powerful and popular learning models that achieve state-of-the-art pattern recognition performance on many computer vision, speech, and language processing tasks.

• However, these networks have also been shown susceptible to carefully crafted adversarial perturbations which force misclassification of the inputs.
• Adversarial examples enable adversaries to subvert the expected system behavior leading to undesired consequences and could pose a security risk when these systems are deployed in the real world.
• In this work, we focus on deep convolutional neural networks and demonstrate that adversaries can easily craft adversarial examples even without any internal knowledge of the target network.