Introduction of Weighted Local Response Normalization in VOneNets for Improved Robustness Against Adversarial Examples

This study aims to enhance the robustness of neural networks against adversarial examples by introducing a novel approach called weighted local response normalization into a recently developed class of hybrid convolutional neural network vision models known as VOneNets. While conventional methods, such as local response normalization, do not significantly improve robustness to adversarial examples, the proposed weighted local response normalization demonstrates notable advancements in robustness. Experiments conducted using the MNIST and CIFAR-10 datasets revealed that the introduction of weighted local response normalization significantly enhanced robustness to adversarial examples. These findings emphasize the importance of how inhibition is applied, rather than merely its application, in boosting robustness. The study concludes that weighted local response normalization serves as an effective strategy to reduce the effects of noise, ultimately enhancing the robustness of neural networks against adversarial examples.