Fortifying Neural Networks: Layer-Specific Strategies for Adversarial Resilience

Adversarial attacks pose significant threats to neural networks, especially in security-critical applications. These attacks can manipulate input data with subtle perturbations, leading to substantial performance degradation by disrupting gradient flow across network layers. This study introduces a layerspecific fortification strategy that targets vulnerable layers within neural architectures. By applying tailored perturbations and implementing resilience mechanisms at identified weak points, our approach maintains robust gradient propagation even under adversarial conditions. We conduct comprehensive evaluations on diverse datasets demonstrating enhanced model robustness and performance. Our findings highlight the efficacy of localized defenses over traditional global adjustment methods, offering a scalable solution for building more secure AI systems.