Assessing and Improving DNN Robustness Against Adversarial Examples From the Perspective of Fully Connected Layers

Summary

Researchers propose a redundant fully connected layer that can be plugged into existing image classification model backbones to improve adversarial robustness. The layer is trained with a loss function that uses cosine similarity to maximize the difference and diversity among multiple fully connected parts. Tested against 12 representative white-box and black-box attacks on two popular datasets, the approach reportedly gives significant robustness with negligible extra training overhead and hardly degrades clean-sample accuracy.

Mitigation

The proposed mitigation is the redundant fully connected layer, combined with existing model backbones in a pluggable manner, trained with the cosine-similarity-based loss function described in the source.

Topics

• Adversarial machine learning

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