论文标题
通过伪造地区指导改进来改善神经网络验证
Improving Neural Network Verification through Spurious Region Guided Refinement
论文作者
论文摘要
我们提出了一种虚假地区的指导改进方法，以验证深度神经网络的鲁棒性验证。我们的方法首先应用Deeppoly抽象域来分析网络。如果无法验证鲁棒性属性，则结果尚无定论。由于过度评价，从某种意义上说，抽象中的计算区域可能是虚假的，因为它不包含任何真正的反例。我们的目标是确定此类虚假地区，并使用它们来指导抽象的完善。核心思想是利用抽象的获得的约束来推断神经元的新界限。这是通过线性编程技术实现的。随着新的界限，我们迭代地采用了Deeppoly，旨在消除虚假地区。我们已经在原型工具deepsrgr中实施了方法。实验结果表明，可以将大量区域识别为虚假，因此，可以显着提高deeppoly的精度。作为副作用，我们表明我们的方法可以应用于验证定量鲁棒性特性。
We propose a spurious region guided refinement approach for robustness verification of deep neural networks. Our method starts with applying the DeepPoly abstract domain to analyze the network. If the robustness property cannot be verified, the result is inconclusive. Due to the over-approximation, the computed region in the abstraction may be spurious in the sense that it does not contain any true counterexample. Our goal is to identify such spurious regions and use them to guide the abstraction refinement. The core idea is to make use of the obtained constraints of the abstraction to infer new bounds for the neurons. This is achieved by linear programming techniques. With the new bounds, we iteratively apply DeepPoly, aiming to eliminate spurious regions. We have implemented our approach in a prototypical tool DeepSRGR. Experimental results show that a large amount of regions can be identified as spurious, and as a result, the precision of DeepPoly can be significantly improved. As a side contribution, we show that our approach can be applied to verify quantitative robustness properties.
