Least-Effort Adversarial Attack Against Gait-Based Identity Recognition System

In this paper, we propose a least-effort adversarial attack against a gait-based identity recognition system (GIRS). Specifically, we leverage a bilevel optimization framework to characterize this leader-follower Stackelberg game between the attacker and gait recognizer to pursue the Nash equilibrium state with hybrid attack intentions of maximum effectiveness and minimum cost. Then, to tractably solve this NP-hard bilevel problem, we present the duality theory and linearization representation technique to reformulate a computable mixed-integer program and derive a globally optima. Finally, we perform comparison experiments on two public datasets to verify the validity of our attack strategy in stealthily inducing mistaken identities. Empirical results can also shed light on an attack mitigation measure to secure a GIRS for the privacy protection.