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Adversarially Robust Spiking Neural Networks via Conversion from Pretrained Adversarial Artificial Neural Networks


Core Concepts
A novel adversarially robust ANN-to-SNN conversion algorithm that leverages adversarially trained baseline ANNs to efficiently transfer robustness gains into the converted SNN, outperforming state-of-the-art direct SNN adversarial training methods.
Abstract
The content presents a novel approach to achieve adversarial robustness in spiking neural networks (SNNs) by converting adversarially trained artificial neural networks (ANNs) into SNNs. The key highlights are: The authors propose an ANN-to-SNN conversion algorithm that initializes the SNN with weights from a robustly pre-trained baseline ANN, and then adversarially finetunes both the synaptic connectivity weights and the layer-wise firing thresholds of the SNN. The method allows integrating any existing robust learning objective developed for conventional ANNs, such as TRADES or MART, into the ANN-to-SNN conversion process, thus optimally transferring robustness gains into the SNN. The authors introduce a novel approach to incorporate adversarially pre-trained ANN batch-norm layer parameters within the spatio-temporal SNN batch-norm operations, without the need to omit these layers. To rigorously evaluate SNN robustness, the authors propose an ensemble attack strategy that simulates adaptive adversaries based on different differentiable approximation techniques for the SNN's non-differentiable spike function. Extensive experiments show that the proposed approach achieves a scalable state-of-the-art solution for adversarial robustness in deep SNNs, outperforming recently introduced end-to-end adversarial training based algorithms with up to 2× larger robustness gains and reduced robustness-accuracy trade-offs.
Stats
The content does not provide any specific numerical data or statistics. The key results are presented in the form of comparative robustness evaluations between the proposed method and baseline approaches.
Quotes
The content does not contain any striking quotes that support the key logics.

Deeper Inquiries

How can the proposed adversarial finetuning approach be extended to incorporate other robust learning objectives beyond TRADES and MART, and what would be the potential benefits and challenges

The proposed adversarial finetuning approach can be extended to incorporate other robust learning objectives by adapting the finetuning objective function to include the desired objective terms. For example, if we want to incorporate a different robust learning objective like PGD-based adversarial training, we can modify the finetuning objective to include the PGD loss term along with the clean loss term. This would involve optimizing the model parameters to minimize both the clean loss and the PGD loss simultaneously during the finetuning process. The potential benefits of incorporating other robust learning objectives include the ability to leverage the strengths of different training methods to enhance the overall robustness of the SNN. By combining multiple robust training objectives, the SNN can potentially achieve a more comprehensive defense against adversarial attacks from different perspectives. This can lead to improved generalization and robustness of the SNN across various attack scenarios. However, there are challenges in extending the approach to incorporate other robust learning objectives. One challenge is the complexity of designing a unified objective function that effectively combines multiple objectives without introducing conflicts or trade-offs between them. Balancing the different objectives and their respective hyperparameters to achieve optimal performance can be a non-trivial task. Additionally, the computational cost of training the SNN with multiple robust objectives may increase, requiring careful optimization strategies to ensure efficiency without sacrificing performance.

What are the limitations of the current ensemble attack strategy, and how can it be further improved to provide even more rigorous evaluations of SNN robustness

The current ensemble attack strategy has some limitations that can be further improved to provide more rigorous evaluations of SNN robustness. One limitation is the reliance on a predefined set of surrogate gradient functions for generating adversarial examples. While the ensemble approach considers different surrogate gradients, it may still not cover the full spectrum of possible gradient variations that could impact the robustness of the SNN. To improve the ensemble attack strategy, one approach could be to dynamically adapt the choice of surrogate gradient functions during the attack based on the model's response. By monitoring the effectiveness of different surrogate gradients in generating successful adversarial examples, the ensemble can prioritize the most effective gradients and adjust the attack strategy accordingly. Another improvement could involve incorporating more diverse attack strategies beyond gradient-based methods. Including non-gradient-based attacks, such as decision-based attacks or query-efficient attacks, can provide a more comprehensive evaluation of the SNN's robustness against different types of adversaries. Furthermore, introducing adversarial training during the ensemble evaluation process, where the SNN is exposed to adversarial examples generated with different strategies and gradients, can help improve the model's robustness by iteratively strengthening its defenses against a variety of attacks.

Given the demonstrated advantages of the conversion-based approach, what are the potential avenues to combine it with direct SNN adversarial training methods to achieve even stronger robustness, while maintaining the benefits of low-latency and energy-efficiency

To combine the conversion-based approach with direct SNN adversarial training methods for stronger robustness while maintaining low-latency and energy-efficiency, a hybrid training strategy can be implemented. This hybrid approach can leverage the benefits of both conversion-based initialization and direct adversarial training to enhance the robustness of the SNN. One potential avenue is to use the conversion-based approach to initialize the SNN with robust weights transferred from a pre-trained ANN. Subsequently, the SNN can undergo direct adversarial training to further enhance its robustness against adversarial attacks. During this adversarial training phase, the SNN can be exposed to various adversarial examples generated using different attack strategies to improve its resilience. Additionally, techniques like curriculum learning can be employed, where the SNN is gradually exposed to increasingly challenging adversarial examples during training. This gradual exposure can help the SNN learn more robust features and defenses against sophisticated attacks while still benefiting from the efficient initialization provided by the conversion-based approach. By combining the strengths of both approaches, the hybrid training strategy can potentially achieve a higher level of robustness in the SNN while preserving its low-latency and energy-efficient properties.
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