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Continual Adversarial Defense: Overcoming Catastrophic Forgetting in Defending Against Evolving Attacks


Core Concepts
Continual adversarial defense is crucial for ensuring the reliability of deep neural networks in real-world deployment scenarios, where new attacks can emerge in sequences. The authors propose a lifelong defense baseline called Anisotropic & Isotropic Replay (AIR) to alleviate catastrophic forgetting when adapting to new attacks.
Abstract
The content discusses the challenge of achieving continual adversarial robustness under attack sequences. It first verifies that adaptation to new attacks can lead to catastrophic forgetting of previous attacks. To address this issue, the authors propose AIR as a memory-free continual adversarial defense baseline. Key highlights: Existing adversarial defense methods are limited to one-shot settings and cannot adapt to new attacks, resulting in insufficient robustness for potential attack sequences. The authors validate the catastrophic forgetting of standard adversarial training under attack sequences, highlighting the need for continual adversarial defense. AIR combines isotropic and anisotropic data augmentation to alleviate catastrophic forgetting within a self-distillation pseudo replay paradigm. The isotropic augmentation helps break specific adversarial patterns, while the anisotropic mix-up augmentation provides richer fusion semantics. A regularizer is introduced to optimize the trade-off between plasticity and stability by aligning the hidden-layer features of new and pseudo-replay attacks. Experiments demonstrate that AIR can approximate or even exceed the empirical performance upper bounds achieved by Joint Training, a commonly used continual learning method.
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Key Insights Distilled From

by Yuhang Zhou,... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01828.pdf
Defense without Forgetting

Deeper Inquiries

How can the regularization effect of previous knowledge on new tasks be leveraged to further improve continual adversarial defense

To leverage the regularization effect of previous knowledge on new tasks for further enhancing continual adversarial defense, a few strategies can be implemented. Firstly, incorporating a knowledge distillation approach where the knowledge learned from previous tasks is transferred to the new model can be beneficial. This can help in retaining important information from the past while adapting to new adversarial challenges. Additionally, utilizing techniques like parameter isolation or feature extraction to preserve essential features learned from previous tasks can aid in maintaining robustness against evolving attacks. By ensuring that the model retains knowledge from past experiences while adapting to new tasks, the regularization effect of previous knowledge can be effectively leveraged to improve continual adversarial defense.

Can the insights from addressing the 'accuracy-robustness' trade-off be applied to mitigate the 'plasticity-stability' dilemma in continual adversarial defense

Insights from addressing the 'accuracy-robustness' trade-off can indeed be applied to mitigate the 'plasticity-stability' dilemma in continual adversarial defense. Just as techniques like TRADES align the output of clean and adversarial samples to enhance robustness, a similar approach can be adopted in continual defense. By aligning the output preferences of new and old models in an implicit chain-like manner, similar to TRADES, the model can maintain stability while adapting to new adversarial challenges. This indirect alignment can help in harmonizing the feature distribution of different attacks belonging to the same label, thereby optimizing the trade-off between plasticity and stability. By focusing on aligning output preferences and feature distributions, the model can effectively balance the need for adaptability with the requirement for stability in continual adversarial defense.

What other potential techniques, beyond the proposed AIR, can be explored to enable continual adversarial defense in real-world deployment scenarios with evolving attack threats

Beyond the proposed AIR model, several other potential techniques can be explored to enable continual adversarial defense in real-world deployment scenarios with evolving attack threats. One approach could involve leveraging meta-learning techniques to quickly adapt to new attacks while retaining knowledge from previous tasks. Additionally, employing generative models for data augmentation to create diverse and realistic adversarial samples can enhance the model's robustness. Continual reinforcement learning methods can also be explored to enable the model to adapt to new attacks over time. Furthermore, techniques like ensemble learning, where multiple models are combined to make predictions, can provide a more robust defense mechanism against a variety of adversarial attacks. By combining these approaches and exploring novel techniques, continual adversarial defense can be strengthened to effectively combat evolving threats in real-world scenarios.
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