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Bilevel Optimized Collusion Attacks Against Gait Recognizer | IEEE Journals & Magazine | IEEE Xplore

Bilevel Optimized Collusion Attacks Against Gait Recognizer


Abstract:

Extensive investigations have revealed that the gait recognition system is always vulnerable to impersonation attacks, which pose significant threats to the identity acce...Show More

Abstract:

Extensive investigations have revealed that the gait recognition system is always vulnerable to impersonation attacks, which pose significant threats to the identity access security. Previous impersonation strategies have primarily focused on mimicking the victim’s walking style or probing the similar gait features to merely manipulate the input samples, without concurrently undermining the built-in model of the gait recognizer, thereby failing to achieve cost-effective attacks. In contrast to these existing heuristic approaches, we propose an optimal adversarial complicity strategy, called collusion attack, which leverages the tight collaboration between an external attacker and an internal spy to tie up into the close colluder, simultaneously enabling the input-&model-corrupted tampering modes and misleading the gait recognizer more powerfully and stealthily for misidentifying the illegitimate Alice as legitimate Bob. Specifically, we formulate a bilevel optimization problem to model such a leader-follower Stackelberg game with sequentially adversarial interaction process between the colluders and gait recognizer. Further, to solve this challenging bilevel problem efficiently, we absorb the Lagrangian dual theory and linearization representation method to reformulate a tractable mixed integer program. Finally, we perform comparison and ablation experiments with the state-of-the-art attack modes on single-&multi-source gait datasets to verify the validity of our collusion strategy in inducing the mistaken identity with great success rate, high confidence, and low cost. Empirical results also shed light on key insights in mitigating the collusion attacks and enhancing the gait recognition robustness to safeguard the identity access applications.
Page(s): 574 - 588
Date of Publication: 11 December 2024

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