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July 1, 2020399 citations

Reluplex made more practical: Leaky ReLU

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JXJin XuZLZishan LiBDBowen Du

Key Points

  • To develop a formal verification algorithm capable of verifying the safety and output guarantees of deep neural networks that utilize the Leaky ReLU activation function.
  • Extended the foundational Reluplex verification framework to support Leaky ReLU activations.
  • Formulated mathematical constraints within the solver to handle non-zero negative slopes and prevent dead neuron issues.
  • Introduced the Leaky-Reluplex algorithm, enabling rigorous formal verification for networks using Leaky ReLU.
  • Provided a verification mechanism that addresses the dead neuron vulnerability inherent to standard ReLU architectures.

Abstract

In recent years, Deep Neural Networks (DNNs) have been experiencing rapid development and have been widely used in various fields. However, while DNNs have shown strong capabilities, their security problems have gradually been exposed. Therefore, the formal guarantee of neural network output is needed. Prior to the appearance of the Reluplex algorithm, the verification of DNNs was always a difficult problem. Reluplex algorithm is specially used to verify DNNs with ReLU activation function. This is an excellent and effective algorithm, but it cannot verify more activation functions. ReLU activation function will bring about "Dead Neuron" problem, and Leaky ReLU activation function can solve this problem, so it is necessary to verify DNNs based on Leaky ReLU activation function. Therefore, we propose the Leaky-Reluplex algorithm, which is based on the Reluplex algorithm. Leaky-Reluplex algorithm can verify DNNs based on Leaky ReLU activation function.

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Cite This Study

Xu et al. (2020) studied this question.

synapsesocial.com/papers/6a0f8b86d8c5cf602efcd81ehttps://doi.org/10.1109/iscc50000.2020.9219587
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