When Deep Neural Networks (DNNs) are used in safety-critical systems, engineers should determine the safety risks associated with failures (i.e., erroneous outputs) observed during testing. For DNNs processing images, engineers visually inspect all failure-inducing images to determine common characteristics among them. Such characteristics correspond to hazard-triggering events (e.g., low illumination) that are essential inputs for safety analysis. Though informative, such activity is expensive and error-prone. To support such safety analysis practices, we propose SEDE, a technique that generates readable descriptions for commonalities in failure-inducing, real-world images and improves the DNN through effective retraining. SEDE leverages the availability of simulators, which are commonly used for cyber-physical systems. It relies on genetic algorithms to drive simulators towards the generation of images that are similar to failure-inducing, real-world images in the test set; it then employs rule learning algorithms to derive expressions that capture commonalities in terms of simulator parameter values. The derived expressions are then used to generate additional images to retrain and improve the DNN. With DNNs performing in-car sensing tasks, SEDE successfully characterized hazard-triggering events leading to a DNN accuracy drop. Also, SEDE enabled retraining leading to significant improvements in DNN accuracy, up to 18 percentage points.
翻译:当深神经网络(DNN)用于安全临界系统时,工程师应确定测试中观察到的故障(即错误产出)产生的安全风险。对于DNNS的图像,工程师要对所有引导失败的图像进行视觉检查,以确定它们之间的共同特征。这些特征相当于作为安全分析重要投入的危险触发事件(例如低光度),虽然信息丰富,但这种活动费用昂贵且容易出错。为了支持这种安全分析做法,我们建议SEDE,这是一种在测试中生成出故障导出真实世界图像的共同点的可读描述技术,并通过有效的再培训来改进DNNNN。S利用模拟器的可用性,这些模拟器通常用于网络物理系统。这些特征与作为安全分析重要投入的灾害触发事件(例如低光度)相对对应。虽然这种活动信息丰富,但这种活动费用昂贵且容易出错。我们建议SEDE,这种技术可以生成出在模拟参数中具有共点的可读性描述,然后通过有效的再培训来生成更多的图像,使DDDDDD能够成功地进行重大的SEN的精确度。