Adversarial Robustness under Long-Tailed Distribution (2104.02703v3)

Abstract: Adversarial robustness has attracted extensive studies recently by revealing the vulnerability and intrinsic characteristics of deep networks. However, existing works on adversarial robustness mainly focus on balanced datasets, while real-world data usually exhibits a long-tailed distribution. To push adversarial robustness towards more realistic scenarios, in this work we investigate the adversarial vulnerability as well as defense under long-tailed distributions. In particular, we first reveal the negative impacts induced by imbalanced data on both recognition performance and adversarial robustness, uncovering the intrinsic challenges of this problem. We then perform a systematic study on existing long-tailed recognition methods in conjunction with the adversarial training framework. Several valuable observations are obtained: 1) natural accuracy is relatively easy to improve, 2) fake gain of robust accuracy exists under unreliable evaluation, and 3) boundary error limits the promotion of robustness. Inspired by these observations, we propose a clean yet effective framework, RoBal, which consists of two dedicated modules, a scale-invariant classifier and data re-balancing via both margin engineering at training stage and boundary adjustment during inference. Extensive experiments demonstrate the superiority of our approach over other state-of-the-art defense methods. To our best knowledge, we are the first to tackle adversarial robustness under long-tailed distributions, which we believe would be a significant step towards real-world robustness. Our code is available at: https://github.com/wutong16/Adversarial_Long-Tail .

Adversarial Robustness under Long-Tailed Distribution

The paper "Adversarial Robustness under Long-Tailed Distribution" addresses a critical gap in the research on adversarial robustness by examining the effects of long-tailed data distributions on the adversarial vulnerability and defense capabilities of deep neural networks. While most prior studies have focused on balanced datasets like CIFAR and ImageNet, this paper recognizes that real-world data often follow a long-tailed distribution, posing unique challenges for both recognition performance and adversarial robustness.

Key Contributions

1. Impact of Imbalanced Data: The authors identify the intrinsic challenges posed by imbalanced data, specifically the negative impacts on recognition accuracy and adversarial robustness. They demonstrate that natural accuracy declines from head to tail classes in long-tailed distributions, thus complicating the task of improving robust accuracy.
2. RoBal Framework: The paper proposes a novel framework, named RoBal, to address the identified challenges. This framework includes two essential modules: a scale-invariant classifier and data re-balancing techniques applied during both training and inference. By incorporating margin engineering techniques and boundary adjustments, RoBal effectively addresses the intrinsic biases introduced by long-tailed distributions.
3. Insights into Long-Tailed Defense Mechanisms: Through a systematic paper of existing long-tailed recognition strategies in conjunction with adversarial training frameworks, the authors derive several critical observations. These include the relative ease of improving natural accuracy, the misleading gains in robust accuracy under unreliable evaluations, and the boundary errors that limit robustness improvements.

Strong Empirical Results

The paper presents extensive experimental results demonstrating the superiority of the RoBal approach over state-of-the-art defense methods. Notably, RoBal achieves substantial improvements in both natural and robust accuracy on long-tailed versions of CIFAR-10 and CIFAR-100 datasets. These results underscore the efficacy of integrating scale-invariant classifiers and re-balancing strategies to enhance adversarial robustness in practical, imbalanced scenarios.

Theoretical Implications

From a theoretical perspective, the paper contributes to understanding the relationship between natural accuracy and robust accuracy, particularly in imbalanced settings. The authors illustrate how boundary error ($\mathbf{R}_{bdy}$) acts as a critical factor in robust accuracy, demonstrating the inherent compromise between improving natural accuracy and maintaining adversarial robustness. By adopting a holistic view that incorporates feature norms, classifier weight norms, and re-balancing strategies, the paper advances the theoretical discourse on achieving robust and balanced predictions.

Future Directions

This paper opens several avenues for future research. One potential direction is to explore the effects of various adversarial training methods on long-tailed distributions further, specifically investigating how such methods could be tailored to provide more targeted improvements for tail classes. Additionally, the integration of self-supervised or semi-supervised learning paradigms with adversarial training frameworks may provide richer representations that enhance adversarial robustness across more diverse and imbalanced datasets.

In conclusion, this paper represents a significant step toward understanding and improving adversarial robustness in the presence of long-tailed distributions. By highlighting the interplay between data imbalance, adversarial vulnerability, and recognition performance, the work paves the way for more resilient and generalizable machine learning models that can operate effectively in real-world environments.