Protecting neural networks from adversarial attacks - Tan, Bryan Bing Xing (Nanyang Technological University, 2020)
As modern technology is rapidly progressing, more applications are utilizing aspects of machine learning—especially deep learning to time-critical and real-world applications. Adversaries are coming up with new ways to exploit attack surfaces in the machine learning process, rendering systems and applications to be ineffective. By using carefully crafted adversarial examples, an adversary can cause even the most well-trained model to fail. Current defences implementations do not seem to cover all the attack surfaces, resulting in the need to understand the concepts behind adversarial attacks even further, to develop a strategic defence mechanism.