Verification of deep neural networks has witnessed a recent surge of interest, fueled by success stories in diverse domains and by abreast concerns about safety and security in envisaged applications. Complexity and sheer size of such networks are challenging for automated formal verification techniques which, on the other hand, could ease the adoption of deep networks in safety- and security-critical contexts. In this paper we focus on enabling state-of-the-art verification tools to deal with neural networks of some practical interest. We propose a new training pipeline based on network pruning with the goal of striking a balance between maintaining accuracy and robustness while making the resulting networks amenable to formal analysis. The results of our experiments with a portfolio of pruning algorithms and verification tools show that our approach is successful for the kind of networks we consider and for some combinations of pruning and verification techniques, thus bringing deep neural networks closer to the reach of formally-grounded methods.

中文翻译：

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神经网络的验证：通过修剪增强可扩展性

深度神经网络的验证见证了最近的兴趣激增，这得益于不同领域的成功案例以及对设想应用中的安全和安保的最新关注。这种网络的复杂性和庞大的规模对自动形式验证技术具有挑战性，另一方面，这可以简化在安全和安全关键环境中采用深度网络。在本文中，我们专注于启用最先进的验证工具来处理具有一些实际意义的神经网络。我们提出了一种基于网络修剪的新训练管道，目的是在保持准确性和鲁棒性之间取得平衡，同时使生成的网络适合正式分析。