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Adversarial Information Bottleneck.

Penglong Zhai, Shihua Zhang

IEEE Transactions on Neural Networks and Learning Systems

|May 20, 2022

Summary

We introduce Adversarial Information Bottleneck (AIB) to improve deep learning robustness against adversarial attacks. AIB optimizes information compression and prediction, enhancing model resilience by finding the optimal hyperparameter tradeoff.

Area of Science:

Machine Learning
Deep Learning Theory
Information Theory

Background:

The Information Bottleneck (IB) principle explains deep learning via compression-prediction tradeoff, controlled by a hyperparameter.
Optimizing IB for robustness and understanding compression effects remain challenges.
Prior methods using noise struggle with adversarial perturbations.

Purpose of the Study:

Propose Adversarial Information Bottleneck (AIB) for enhanced robustness.
Investigate the impact of the tradeoff hyperparameter on compression and prediction.
Analyze adversarial robustness across different IB methods.

Main Methods:

Developed an Adversarial Information Bottleneck (AIB) method.
Optimized AIB using a min-max optimization problem without distribution assumptions.

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Conducted numerical experiments on synthetic and real-world datasets.

Main Results:

AIB learns more invariant representations and mitigates adversarial perturbations effectively.
Demonstrated superior performance compared to competing IB methods.
Identified that IB models at the 'knee point' of the IB curve offer optimal robustness.

Conclusions:

AIB provides a robust framework for deep learning by balancing compression and prediction.
The hyperparameter tradeoff significantly influences model robustness.
IB models exhibiting optimal compression-prediction balance show enhanced adversarial resilience.