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DEEP REINFORCEMENT LEARNING BASED METHOD FOR SURREPTITIOUSLY GENERATING SIGNALS TO FOOL A RECURRENT NEURAL NETWORK
DEEP REINFORCEMENT LEARNING BASED METHOD FOR SURREPTITIOUSLY GENERATING SIGNALS TO FOOL A RECURRENT NEURAL NETWORK
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机译:基于深度加强学习的方法,用于偷偷发电信号欺骗复发性神经网络
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摘要
Described is an attack system for generating perturbations of input signals in a recurrent neural network (RNN) based target system using a deep reinforcement learning agent to generate the perturbations. The attack system trains a reinforcement learning agent to determine a magnitude of a perturbation with which to attack the RNN based target system. A perturbed input sensor signal having the determined magnitude is generated and presented to the RNN based target system such that the RNN based target system produces an altered output in response to the perturbed input sensor signal. The system identifies a failure mode of the RNN based target system using the altered output.
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