How to Mitigate Adversarial Attacks to Computer Vision Systems

CNN, specialized neural networks for Computer Vision tasks, are used in sensitive contexts and exposed in the wild. While extremely accurate, they are also sensitive to imperceptible perturbations that can’t be detected by human eyes. For this reason they have been targeted by hackers which implemented AI-based techniques for their malicious purposes. During the presentation I am going to explain defense strategies to mitigate the effect of such attacks and make neural networks more robust to them, while at the same time keeping minimal impact on the accuracy of the model and implementation costs.