Feature Separation and Recalibration for Adversarial Robustness (2303.13846v1)

Abstract: Deep neural networks are susceptible to adversarial attacks due to the accumulation of perturbations in the feature level, and numerous works have boosted model robustness by deactivating the non-robust feature activations that cause model mispredictions. However, we claim that these malicious activations still contain discriminative cues and that with recalibration, they can capture additional useful information for correct model predictions. To this end, we propose a novel, easy-to-plugin approach named Feature Separation and Recalibration (FSR) that recalibrates the malicious, non-robust activations for more robust feature maps through Separation and Recalibration. The Separation part disentangles the input feature map into the robust feature with activations that help the model make correct predictions and the non-robust feature with activations that are responsible for model mispredictions upon adversarial attack. The Recalibration part then adjusts the non-robust activations to restore the potentially useful cues for model predictions. Extensive experiments verify the superiority of FSR compared to traditional deactivation techniques and demonstrate that it improves the robustness of existing adversarial training methods by up to 8.57% with small computational overhead. Codes are available at https://github.com/wkim97/FSR.

• The paper introduces Feature Separation and Recalibration (FSR), a method to partition and recalibrate distorted non-robust features for improved adversarial robustness in deep neural networks.
• FSR employs a learnability-guided separation stage and a recalibration stage using differentiable Gumbel-softmax masks to process features without gradient masking.
• Empirical evaluations show FSR improves robustness up to 8.57% against various attacks on datasets like CIFAR-10 and is adaptable to existing adversarial training methods.

Examination of Feature Separation and Recalibration for Enhanced Adversarial Robustness

The paper "Feature Separation and Recalibration for Adversarial Robustness" by Kim, Cho, Jung, and Yoon addresses the critical issue of adversarial robustness in deep neural networks (DNNs). The proposed solution, Feature Separation and Recalibration (FSR), is a method designed to recalibrate distorted, non-robust features that occur during adversarial attacks, aiming to restore useful discriminative cues for accurate predictions. This research is timely given the susceptibility of DNNs to adversarial examples, where small perturbations can lead to significant mispredictions.

Theoretical and Methodological Insights

The premise of the paper is that existing defense techniques predominantly focus on deactivating or negating non-robust activations, which may inadvertently discard useful information. Contrary to conventional methods, FSR distinguishes itself by not just identifying these non-robust features but actively recalibrating them to retain discriminative information lost during adversarial attacks. The paper introduces a Separation stage, which partitions the input features into robust and non-robust components using a learnability-guided scoring mechanism. Subsequently, the Recalibration stage modifies the non-robust features to extract further valuable cues.

The authors employ a differentiable mask using Gumbel-softmax to prevent the classical problem of gradient masking associated with binary activation methods. This nuance ensures that the recalibration phase does not inadvertently introduce gradient obfuscation, maintaining the defensive capability under rigorous evaluation criteria.

Empirical Evaluations

FSR demonstrates significant empirical success, showing robustness improvements up to 8.57% over traditional adversarial defenses across various model architectures and datasets like CIFAR-10 and SVHN. Notably, the authors evaluate the approach against a wide range of attacks, including FGSM, PGD, C&W, and AutoAttack, illustrating its adaptability to both white-box and black-box settings.

The paper meticulously details the robustness assessment methodology, leveraging an ensemble measurement strategy to provide comprehensive evaluations. This aspect underscores the real-world applicability of the proposed defense, where it is critical to assess robustness across diverse attack types.

Implications and Future Directions

FSR's adaptability to various existing adversarial training protocols, be it AT, TRADES, or MART, signifies its potential to enhance current state-of-the-art methods with minimal computational overhead. This quality is particularly critical for practical deployment in resource-constrained environments. The approach also shows promise for further extension into complex, high-resolution datasets beyond CIFAR-10, such as Tiny ImageNet, suggesting that FSR is not limited by model scale or dataset complexity.

One limitation highlighted is the occasional drop in natural image accuracy due to the recalibration process not naturally discriminating between malicious and benign perturbations. Addressing this issue could involve integrating curricula or adaptive learning strategies that discern perturbation intent more effectively.

Conclusion

This work presents a sophisticated enhancement to adversarial robustness techniques, providing empirical evidence and theoretical groundwork for a recalibration-based approach that preserves useful feature activations. By addressing both robust and non-robust activations within adversarial contexts, FSR represents a meaningful advancement in crafting resilient AI systems against adversarial perturbations while maintaining operational efficiency. Subsequent research could explore deeper optimization strategies for the recalibration stage and integrate FSR with emerging robust AI paradigms, potentially unlocking broader applications across various AI-driven fields.