Investigating the causes of the vulnerability of CNNs to adversarial perturbations: learning objective, model components, and learned representations

• Main Author: Coppola, Davide
• Other Authors: Guan Cuntai
• Format: Thesis-Master by Research
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
• Online Access: https://hdl.handle.net/10356/171336
• Institution: Nanyang Technological University

This work focuses on understanding how adversarial perturbations can disrupt the behavior of Convolutional Neural Networks (CNNs). Here, it is hypothesized that some components may be more vulnerable than others, unlike other research that considers a model vulnerable as a whole. Identifying model-s...