Adversarial Rademacher Complexity of Deep Neural Networks

2022-11-27 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, short

Summary

The paper "Adversarial Rademacher Complexity of Deep Neural Networks" by Xiao et al. addresses the significant challenge of robust generalization in deep neural networks (DNNs) when faced with adversarial attacks. While DNNs can learn from perturbed training data, their ability to generalize to unseen perturbed test data remains limited. This research provides the first theoretical bound on Adversarial Rademacher Complexity (ARC) for deep neural networks, a problem previously unresolved beyond linear functions and two-layer architectures. The authors introduce a novel concept of "intermediate adversarial examples" and a compatible framework for calculating covering numbers to achieve this bound. Experimental analysis further demonstrates that the weight norm is a crucial factor influencing the robust generalization gap in DNNs.

Key takeaway

For AI Scientists designing robust deep learning models, this research provides critical theoretical grounding for understanding adversarial generalization. You should recognize that the weight norm significantly influences your model's ability to generalize to perturbed test data. Consider incorporating regularization techniques that control weight norm to improve robust performance, especially when facing sophisticated adversarial attacks. This work offers a new lens for evaluating and enhancing model resilience beyond empirical methods.

Key insights

This paper establishes the first theoretical bound for Adversarial Rademacher Complexity in deep neural networks, linking robust generalization to weight norm.

Principles

• DNNs struggle with robust generalization to perturbed test data.
• Weight norm is crucial for robust generalization in DNNs.
• Adversarial Rademacher Complexity can quantify robust generalization.

Method

The paper introduces "intermediate adversarial examples" and a new covering number calculation framework to derive ARC bounds for deep neural networks.

In practice

• Focus on controlling weight norm for robust DNNs.
• Apply ARC bounds to analyze model robustness.
• Design adversarial training methods using intermediate examples.

Topics

• Deep Neural Networks
• Adversarial Robustness
• Rademacher Complexity
• Generalization Bounds
• Adversarial Examples
• Weight Norm

Best for: Research Scientist, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.