Shipped: Defending Classical Neural Networks Against Adversarial AI

An open-source AI security research proof of concept investigating how adversarial training can improve the robustness of classical Convolutional Neural Networks (CNNs).

The framework trains a neural network for handwritten digit recognition and evaluates its performance against multiple white-box adversarial attacks.

Attack techniques include Fast Gradient Sign Method (FGSM), Carlini-Wagner (CW), and Projected Gradient Descent (PGD), along with compounded attack combinations.

The project applies adversarial training by incorporating manipulated examples into the model's training process, allowing researchers to compare classification performance before and after defensive training.

The framework supports handwritten digit datasets including MNIST and EMNIST, with additional dataset options documented in the repository.

Experiments examine model accuracy, adversarial misclassification, and the effectiveness of defensive training under selected attack conditions.

Designed for AI security researchers, machine learning engineers, students, and educators exploring practical adversarial robustness techniques.

This is an educational research demonstration rather than a certified production defense. Its findings do not establish robustness against all possible attacks or adaptive adversaries.