A Robust Neural Network against Adversarial Attacks
Mohammad Barr · Engineering Technology & Applied Science Research · 2025
The security and dependability of neural network designs are increasingly jeopardized by adversarial attacks, which can cause false positives, degrade performance, and disrupt applications, particularly on resource-constrained Internet of Things (IoT) devices. Τhis study adopts a two-step approach: first, designs a robust Convolutional Neural Network (CNN) that achieves high performance on the MNIST dataset, and second, evaluates and enhances its resilience against advanced adversarial techniques such as Deepfool and L-BFGS. Initial evaluations revealed that while the proposed CNN performs well on standard classification tasks, it is vulnerable to adversarial attacks. To mitigate this vulnerability, APE-GAN, an innovative adversarial training technique, was employed to re-train the proposed CNN, significantly improving its robustness against adversarial attacks while optimizing performance for embedded systems with limited computational resources. Systematic experimentation demonstrates the effectiveness of APE-GAN in enhancing both the accuracy and resilience of the proposed CNN, outperforming conventional methods and establishing it as a pioneering solution in adversarial machine learning. By integrating APE-GAN into the training process, this research ensures the secure and efficient operation of the proposed CNN in real-world IoT applications, marking a significant step forward in addressing the challenges posed by adversarial attacks.