Nearest Neighbor Projection Removal Adversarial Training

Deep neural networks used for image classification are vulnerable to adversarial examples (slightly altered images designed to fool AI models). This paper proposes a new adversarial training method that improves robustness by reducing inter-class feature overlap (the problem where the AI's internal representations of different image categories get too close together), making it harder for adversarial attacks to work. The method was tested on standard image datasets and showed competitive performance compared to existing adversarial training techniques.