神经网络的正确性验证
Correctness Verification of Neural Networks
摘要：
我们提出了一个新的框架，指定和验证的正确性全局神经网络的感知任务。感知任务的神经网络验证大多集中在鲁棒性验证上。与鲁棒性验证不同，鲁棒性验证旨在验证网络的预测在标记点周围的某些局部区域中是稳定的，我们的框架提供了一种在整个目标输入空间中全局指定正确性的方法，并验证网络对所有目标输入都是正确的（或找到网络不正确的区域）。我们通过1）由世界的所有相关状态组成的状态空间和2）从世界的状态产生神经网络输入的观察过程来提供规范。用有限数量的瓦片平铺状态和输入空间，从状态瓦片获得地面真值边界，从输入瓦片获得网络输出边界，然后比较地面真值和网络输出边界，为任何感兴趣的输入提供网络输出误差的上限。所提出的框架还可以检测非法输入--不包含在（或接近）由状态空间和观察过程定义的目标输入空间中的输入（神经网络不是设计来处理它们的），这样我们就可以在没有保证时进行标记。两个案例研究的结果突出了我们的技术在整个目标输入空间验证误差界的能力，并显示了误差界在状态和输入空间的变化。
英文摘要：
We present a novel framework for specifying and verifying correctness globally for neural networks on perception tasks. Most previous works on neural network verification for perception tasks focus on robustness verification. Unlike robustness verification, which aims to verify that the prediction of a network is stable in some local regions around labelled points, our framework provides a way to specify correctness globally in the whole target input space and verify that the network is correct for all target inputs (or find the regions where the network is not correct). We provide a specification through 1) a state space consisting of all relevant states of the world and 2) an observation process that produces neural network inputs from the states of the world. Tiling the state and input spaces with a finite number of tiles, obtaining ground truth bounds from the state tiles and network output bounds from the input tiles, then comparing the ground truth and network output bounds delivers an upper bound on the network output error for any inputs of interest. The presented framework also enables detecting illegal inputs -- inputs that are not contained in (or close to) the target input space as defined by the state space and observation process (the neural network is not designed to work on them), so that we can flag when we don't have guarantees. Results from two case studies highlight the ability of our technique to verify error bounds over the whole target input space and show how the error bounds vary over the state and input spaces.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
