Self-Healing Neural Networks Against Adversarial Attacks
Keywords:
Self-healing neural networks, adversarial attacks, reinforcement learning, dynamic layer pruning, attack signature libraryAbstract
Adversarial attacks represent a significant threat to the stability and accuracy of neural networks, particularly in critical real-time applications such as autonomous vehicles, financial systems, and medical diagnosis. Conventional defensive mechanisms, including adversarial training and gradient masking, are static and fail to adapt to evolving attack patterns. This paper introduces a self-healing neural network framework that integrates dynamic adaptation using reinforcement learning, dynamic layer pruning, and attack signature libraries to improve resilience against adversarial attacks. The proposed approach enables networks to detect and diagnose adversarial perturbations mid-inference and reconfigure their architecture to neutralize threats in real-time. Experimental evaluations show that the framework enhances the robustness of neural networks against white-box, black-box, and transfer-based attacks while maintaining competitive performance in terms of accuracy and computational efficiency.
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