Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Albert Bifet,Jesse Davis,Indr? ?liobait? Confer

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A Theoretically Grounded Extension of?Universal Attacks from?the?Attacker’s ViewpointSpecifically, we embrace the attacker’s perspective and introduce a theoretical bound quantifying how much the universal perturbations are able to fool a given model on unseen examples. An extension to assert the transferability of universal attacks is also provided. To learn such perturbations, we

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Linear Modeling of?the?Adversarial Noise Spaceinguish specific attacks adapted to each example from universal ones, referred to as example-agnostic. Even though specific adversarial attacks are efficient on their target DNN classifier to attack, they struggle to transfer to others. Conversely, universal adversarial attacks suffer from lower att

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