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A white-box adversarial sample generation method and system for a liquid state machine

PendingCN122287702AEffective white box attackavoid queryAlgorithmForward propagation
This invention relates to a white-box adversarial example generation method and system for liquid state machines, belonging to the field of neural network security. It aims to address the problem that existing methods cannot effectively handle non-differentiable cyclic components and gradient calculation failures caused by random pulse coding in liquid state machines. The method constructs a computable and stable gradient propagation path from model loss to the original input through gradient splitting and time-averaged gradient approximation. It includes a cyclic process of forward propagation and backward gradient calculation. The corresponding system includes a data input and preprocessing module, a target model loading and inference module, a gradient calculation module, an attack algorithm integration module, and an adversarial example synthesis and feedback module. This invention achieves an effective white-box attack on liquid state machines for the first time, with advantages such as high attack success rate, good perturbation concealment, and strong scalability, providing a powerful tool for evaluating the security of spiking neural networks.
Owner:NAT INNOVATION INST OF DEFENSE TECH PLA ACAD OF MILITARY SCI