Illusionary Attacks on Sequential Decision Makers and Countermeasures
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Autonomous intelligent agents deployed to the real-world need to be robust against adversarial attacks on sensory inputs. Existing work in reinforcement learning focuses on minimum-norm perturbation attacks, which were originally introduced to mimic a notion of perceptual invariance in computer vision. In this paper, we note that such minimum-norm perturbation attacks can be trivially detected by victim agents, as these result in observation sequences that are not consistent with the victim agent's actions. Furthermore, many real-world agents, such as physical robots, commonly operate under human supervisors, which are not susceptible to such perturbation attacks. As a result, we propose to instead focus on illusionary attacks, a novel form of attack that is consistent with the world model of the victim agent. We provide a formal definition of this novel attack framework, explore its characteristics under a variety of conditions, and conclude that agents must seek realism feedback to be robust to illusionary attacks.
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