  Deep learning models are being integrated into a wide range of high-impact,
security-critical systems, from self-driving cars to medical diagnosis.
However, recent research has demonstrated that many of these deep learning
architectures are vulnerable to adversarial attacks--highlighting the vital
need for defensive techniques to detect and mitigate these attacks before they
occur. To combat these adversarial attacks, we developed UnMask, an adversarial
detection and defense framework based on robust feature alignment. The core
idea behind UnMask is to protect these models by verifying that an image's
predicted class ("bird") contains the expected robust features (e.g., beak,
wings, eyes). For example, if an image is classified as "bird", but the
extracted features are wheel, saddle and frame, the model may be under attack.
UnMask detects such attacks and defends the model by rectifying the
misclassification, re-classifying the image based on its robust features. Our
extensive evaluation shows that UnMask (1) detects up to 96.75% of attacks, and
(2) defends the model by correctly classifying up to 93% of adversarial images
produced by the current strongest attack, Projected Gradient Descent, in the
gray-box setting. UnMask provides significantly better protection than
adversarial training across 8 attack vectors, averaging 31.18% higher accuracy.
We open source the code repository and data with this paper:
https://github.com/safreita1/unmask.
