#OPENBOX - Maura Pintor - Improving Optimization of Adversarial Examples - Part 1
OPENBOX aims to bring an easier understanding of open problems that helps in finding solutions for such problems. For the said purpose, I interview researchers and practitioners who have published works on open problems in various areas of Artificial Intelligence and Machine Learning to collect a simplified understanding of these open problems. These are published as podcast series. Ideas emerge when curiosity meets clarity. Here we go with OPENBOX to bring clarity to those curious minds looking to solve real-world problems. This project is done in collaboration with ForHumanity. ForHumanity is a 501(c)(3) nonprofit organization that minimizes the downside risks of AI and autonomous systems. ForHumanity develops criteria for an independent audit of AI systems. To know more visit https://forhumanity.center/. Today, we have with us, Maura. Maura Pintor is a Postdoctoral Researcher at the PRA Lab, at the University of Cagliari, Italy. She received the MSc degree in Telecommunications Engineering with honors in 2018 and the Ph.D. degree in Electronic and Computer Engineering from the University of Cagliari in 2022. Her Ph.D. thesis, "Towards Debugging and Improving Adversarial Robustness Evaluations", provides a framework for optimizing and debugging adversarial attacks. Maura co-authored a paper titled “Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples, “ which was the podcast's focus. This is part 1 of the podcast. In this podcast, Maura covered why evaluating defenses is complex, the nature of mitigation failures and why robustness is overestimated in evasion attacks in the context of open issues. --- Send in a voice message: https://podcasters.spotify.com/pod/show/ryan-carrier3/message