Adversarial attacks on deep learning - Yee, An Qi (Nanyang Technological University, 2023)
Deep learning models, especially convolutional neural networks (CNNs), have made significant progress in the field of image recognition and classification. However, adversarial attacks have emerged as a significant vulnerability, posing threats to the robustness of these models. One notable example is the one-pixel attack, which leads to incorrect predictions just by changing a single pixel, which could lead to potentially serious consequences. This project aims to investigate the efficiency and effectiveness of different search strategies in conducting the one- pixel attacks on black box networks.