Improving Accuracy-Robustness Trade-off of Neural Classifiers via Adaptive Mixing of Standard and Robust Models

Core Concepts

The core message of this paper is that by mixing the output probabilities of a standard neural network classifier and a robust neural network classifier, the accuracy-robustness trade-off can be significantly alleviated, achieving high clean accuracy while maintaining strong adversarial robustness.

Abstract

The paper proposes a method called "adaptive smoothing" to improve the accuracy-robustness trade-off of neural classifiers. The key insights are:

1. Replacing the K-nearest-neighbor classifier used in prior "locally biased smoothing" work with a robust neural network can significantly boost the performance of the mixed classifier.
2. The mixing ratio between the standard and robust classifiers can be adaptively adjusted using a neural network "mixing network", further improving the accuracy-robustness trade-off.
3. The mixed classifier can be certified to be robust under realistic assumptions on the robustness of the base classifiers.
4. Extensive experiments show that the proposed adaptive smoothing method can achieve state-of-the-art accuracy-robustness trade-off on the CIFAR-100 dataset, outperforming prior works by a large margin.

The paper first generalizes the locally biased smoothing formulation to the multi-class setting and replaces the weak K-NN classifier with a robust neural network. It then proposes to adaptively adjust the mixing ratio between the standard and robust classifiers using a neural network "mixing network". This mixing network is trained to detect adversarial inputs and adjust the mixing ratio accordingly.

The authors provide theoretical analysis to certify the robustness of the mixed classifier under realistic assumptions on the base classifiers. They also conduct extensive experiments to evaluate the empirical performance of the proposed adaptive smoothing method, demonstrating state-of-the-art accuracy-robustness trade-off on the CIFAR-100 dataset.

Stats

The paper reports the following key metrics:

On CIFAR-100, the proposed method achieves 85.21% clean accuracy and 38.72% ℓ∞-AutoAttacked (ϵ = 8/255) accuracy, becoming the second most robust method on the RobustBench benchmark as of submission.
The proposed method improves the clean accuracy by 10 percentage points over all listed models on the RobustBench benchmark.

Quotes

"Despite the emergence of these proposed remedies to the adversarial robustness issue, many practitioners are reluctant to adopt them. As a result, existing publicly available services are still vulnerable [47, 19], presenting severe safety risks."
"Fortunately, recent research has argued that it should be possible to simultaneously achieve robustness and accuracy on benchmark datasets [90]."
"Adaptive smoothing allows for an interpretable continuous adjustment between accuracy and robustness at inference time, which can be achieved by simply adjusting the mixture ratio."

Key Insights Distilled From

Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing

by Yatong Bai,B... at arxiv.org 04-10-2024

https://arxiv.org/pdf/2301.12554.pdf

Deeper Inquiries

How can the proposed adaptive smoothing method be extended to other domains beyond computer vision, such as natural language processing or speech recognition

The adaptive smoothing method proposed in the context of computer vision can be extended to other domains like natural language processing (NLP) or speech recognition by adapting the framework to the specific characteristics of these domains. In NLP, for example, the base classifiers could be pre-trained language models like BERT or GPT, which are known for their strong performance in various NLP tasks. The mixing network could then combine the outputs of these language models to achieve a balance between accuracy and robustness. Additionally, in speech recognition, the base classifiers could be deep neural networks trained on audio data, and the mixing network could adjust the contributions of these classifiers based on the input audio features. By customizing the base classifiers and the mixing network architecture to the requirements of NLP or speech recognition tasks, the adaptive smoothing method can be effectively applied in these domains.

What are the potential limitations or failure modes of the adaptive smoothing approach, and how can they be addressed in future work

One potential limitation of the adaptive smoothing approach could be the complexity of training the mixing network to effectively balance the contributions of the base classifiers. To address this, future work could focus on developing more sophisticated training strategies, such as incorporating reinforcement learning techniques to optimize the mixing network's parameters. Additionally, ensuring the robustness of the mixing network itself against adversarial attacks is crucial. This could be achieved by augmenting the training data with diverse adversarial examples targeting the mixing network specifically. Regularization techniques and adversarial training methods can also be employed to enhance the robustness of the mixing network. Furthermore, conducting extensive experiments on a wide range of datasets and attack scenarios can help identify and mitigate potential failure modes of the adaptive smoothing approach.

Can the theoretical analysis on certified robustness be further strengthened by incorporating additional properties of the base classifiers, such as their local Lipschitzness or the robustness margin

The theoretical analysis on certified robustness can be further strengthened by incorporating additional properties of the base classifiers, such as their local Lipschitzness or the robustness margin. By considering the local Lipschitzness of the base classifiers, the analysis can provide more nuanced insights into the behavior of the mixed classifier in different regions of the input space. Moreover, incorporating the robustness margin of the base classifiers into the analysis can offer a more comprehensive understanding of how the mixed classifier performs under different attack scenarios. By integrating these additional properties into the theoretical framework, the analysis can provide more accurate and detailed guarantees on the certified robustness of the adaptive smoothing method.