SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier Domain

Understand

Despite the success of convolutional neural networks (CNNs) in many computer vision and image analysis tasks, they remain vulnerable against so-called adversarial attacks: Small, crafted perturbations in the input images can lead to false predictions.

• A possible defense is to detect adversarial examples.
• In this work, we show how analysis in the Fourier domain of input images and feature maps can be used to distinguish benign test samples from adversarial images.
• We propose two novel detection methods: Our first method employs the magnitude spectrum of the input images to detect an adversarial attack.