论文标题
构造错误：微调神经网络可确保有限示例的性能
Incorrect by Construction: Fine Tuning Neural Networks for Guaranteed Performance on Finite Sets of Examples
论文作者
论文摘要
使用正式方法来保证深神经网络的可靠性非常有兴趣。但是，这些技术也可用于植入精心选择的输入输出对。我们提出了一种新技术的初步结果，用于使用SMT求解器微调Relu神经网络的权重，以确保在一组有限的特定示例中的结果。该过程可用于确保在关键示例上的性能，但也可以用来插入难以找到的错误示例，以触发意外的性能。我们通过微调MNIST网络来证明这种方法，以错误地对特定图像进行分类，并讨论损害自由共享机器学习模型可靠性的方法的潜力。
There is great interest in using formal methods to guarantee the reliability of deep neural networks. However, these techniques may also be used to implant carefully selected input-output pairs. We present initial results on a novel technique for using SMT solvers to fine tune the weights of a ReLU neural network to guarantee outcomes on a finite set of particular examples. This procedure can be used to ensure performance on key examples, but it could also be used to insert difficult-to-find incorrect examples that trigger unexpected performance. We demonstrate this approach by fine tuning an MNIST network to incorrectly classify a particular image and discuss the potential for the approach to compromise reliability of freely-shared machine learning models.
