论文标题
使用面部格子的馈送神经网络的可及性分析
Reachability Analysis for Feed-Forward Neural Networks using Face Lattices
论文作者
论文摘要
深层神经网络已被广泛应用，作为处理复杂和实际问题的有效方法。但是，最根本的开放问题之一是缺乏分析其行为安全性的形式方法。为了应对这一挑战，我们提出了一种可行的技术，以将神经网络的确切可触手可及集计算为输入集。我们的方法目前重点介绍具有Relu激活功能的前馈神经网络。基于多层的方法的主要挑战之一是确定中间多型与神经元的超平面之间的相交。在这方面，我们提出了一种新的方法，可以用面部晶格（完整的组合结构）构造多面体。通过验证ACAS XU网络和其他基准的安全性，可以评估我们方法的正确性和性能。与Reluplex，Marabou和NNV等最新方法相比，我们的方法表现出明显更高的效率。此外，我们的方法能够构建给定输出集的完整输入集，以便可以跟踪任何导致安全违规的输入。
Deep neural networks have been widely applied as an effective approach to handle complex and practical problems. However, one of the most fundamental open problems is the lack of formal methods to analyze the safety of their behaviors. To address this challenge, we propose a parallelizable technique to compute exact reachable sets of a neural network to an input set. Our method currently focuses on feed-forward neural networks with ReLU activation functions. One of the primary challenges for polytope-based approaches is identifying the intersection between intermediate polytopes and hyperplanes from neurons. In this regard, we present a new approach to construct the polytopes with the face lattice, a complete combinatorial structure. The correctness and performance of our methodology are evaluated by verifying the safety of ACAS Xu networks and other benchmarks. Compared to state-of-the-art methods such as Reluplex, Marabou, and NNV, our approach exhibits a significantly higher efficiency. Additionally, our approach is capable of constructing the complete input set given an output set, so that any input that leads to safety violation can be tracked.
