Did an 45 min long interview with DataSkeptics (one of the biggest data science / ML communities in the world). I spoke about GANs, Adversarial Attacks as well as my recent publication on using GANs to detect and defend against adversarial attacks.

Recent work has shown that state-of-the-art models are highly vulnerable to adversarial perturbations of the input. We propose cowboy, an approach to detecting and defending against adversarial attacks by using both the discriminator and generator of a GAN trained on the same dataset. We show that the discriminator consistently scores the adversarial samples lower than the real samples across multiple attacks and datasets. We provide empirical evidence that adversarial samples lie outside of ...