Deep learning has become a cornerstone of modern computer vision in recent years, fueling advancements in image classification, object detection, and facial recognition. However, with its rise, vulnerabilities inherent to these models have emerged, particularly in the form of adversarial attacks. This presentation delves deep into adversarial machine learning, exploring its implications and techniques for hacking neural networks within the computer vision domain.