Machine Learning Security Seminar Series - Giulio Rossolini
Автор: MLSec
Загружено: 2022-12-13
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Description: Invited talk by Giulio Rossolini (Scuola Superiore Sant'Anna of Pisa)
Title of the talk:
On the Real-World Adversarial Robustness Against Physically-Realizable Attacks
Abstract:
In recent years, adversarial perturbations have become a hot topic in the safe and secure AI community. However, the concrete feasibility of such attacks on critical systems is often questioned, as it is necessary to exploit the digital representation of the input. This fact has inspired novel approaches for injecting adversarial features as physical objects or patches.
This seminar will provide an overview of the most successful strategies for crafting physically-realizable attacks, and examine their transferability among different real-world scenarios and computer vision architectures. Then, the presentation will address empirical and certifiable studies to improve the robustness of deep learning models against these threats.
Bio:
Giulio Rossolini is a Ph.D. student at the Department of Excellence in Robotics & AI and the Real-Time Systems Laboratory (ReTiS Lab) of the Scuola Superiore Sant’Anna of Pisa. His research topics include the design of robust tools and architectures to enhance the trustworthiness of deep learning models in computer vision applications and safety-critical systems.
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