FLARE: Fingerprinting Deep Reinforcement Learning Agents using Universal Adversarial Masks

We propose FLARE, the first fingerprinting mechanism to verify whether a suspected Deep Reinforcement Learning (DRL) policy is an illegitimate copy of another (victim) policy. We first show that it is possible to find non-transferable, universal adversarial masks, i.e., perturbations, to generate adversarial examples that can successfully transfer from a victim policy to its modified versions but not to independently trained policies. FLARE employs these masks as fingerprints to verify the true ownership of stolen DRL policies by measuring an action agreement value over states perturbed via such masks. Our empirical evaluations show that FLARE is effective (100% action agreement on stolen copies) and does not falsely accuse independent policies (no false positives). FLARE is also robust to model modification attacks and cannot be easily evaded by more informed adversaries without negatively impacting agent performance. We also show that not all universal adversarial masks are suitable candidates for fingerprints due to the inherent characteristics of DRL policies. The spatio-temporal dynamics of DRL problems and sequential decision-making process make characterizing the decision boundary of DRL policies more difficult, as well as searching for universal masks that capture the geometry of it.Comment: Will appear in the proceedings of ACSAC 2023; 13 pages, 5 figures, 7 table

Real-time Adversarial Perturbations against Deep Reinforcement Learning Policies: Attacks and Defenses

Recent work has shown that deep reinforcement learning (DRL) policies are vulnerable to adversarial perturbations. Adversaries can mislead policies of DRL agents by perturbing the state of the environment observed by the agents. Existing attacks are feasible in principle but face challenges in practice, for example by being too slow to fool DRL policies in real time. We show that using the Universal Adversarial Perturbation (UAP) method to compute perturbations, independent of the individual inputs to which they are applied to, can fool DRL policies effectively and in real time. We describe three such attack variants. Via an extensive evaluation using three Atari 2600 games, we show that our attacks are effective, as they fully degrade the performance of three different DRL agents (up to 100%, even when the $l_\infty$ bound on the perturbation is as small as 0.01). It is faster compared to the response time (0.6ms on average) of different DRL policies, and considerably faster than prior attacks using adversarial perturbations (1.8ms on average). We also show that our attack technique is efficient, incurring an online computational cost of 0.027ms on average. Using two further tasks involving robotic movement, we confirm that our results generalize to more complex DRL tasks. Furthermore, we demonstrate that the effectiveness of known defenses diminishes against universal perturbations. We propose an effective technique that detects all known adversarial perturbations against DRL policies, including all the universal perturbations presented in this paper.Comment: 13 pages, 6 figure