论文标题
对应用内深度学习模型的自动切片和测试
Automation Slicing and Testing for in-App Deep Learning Models
论文作者
论文摘要
配备了应用程序内深度学习（DL）模型的智能应用程序（IAPPS）正在新兴提供稳定的DL推理服务。但是，应用程序市场在自动测试IAPPS方面遇到麻烦，因为应用程序内模型是带有普通代码的夫妻。在这项工作中，我们提出了一个自动化工具ASTM，该工具可以实现对应用内模型的大规模测试。 ASTM作为输入IAPPS，输出可以替换应用程序内模型作为测试对象。 ASTM提出了两种重建技术，以将应用程序内模型转换为启用反向Propagation版本，并重建用于DL推理的IO处理代码。在ASTM的帮助下，我们对100个独特的商业内应用内模型的鲁棒性进行了大规模研究，发现56 \％的应用程序内模型在我们的背景下容易受到鲁棒性问题的影响。 ASTM还检测到对可能造成经济损失和安全问题的三个代表性IAPPS的身体攻击。
Intelligent Apps (iApps), equipped with in-App deep learning (DL) models, are emerging to offer stable DL inference services. However, App marketplaces have trouble auto testing iApps because the in-App model is black-box and couples with ordinary codes. In this work, we propose an automated tool, ASTM, which can enable large-scale testing of in-App models. ASTM takes as input an iApps, and the outputs can replace the in-App model as the test object. ASTM proposes two reconstruction techniques to translate the in-App model to a backpropagation-enabled version and reconstruct the IO processing code for DL inference. With the ASTM's help, we perform a large-scale study on the robustness of 100 unique commercial in-App models and find that 56\% of in-App models are vulnerable to robustness issues in our context. ASTM also detects physical attacks against three representative iApps that may cause economic losses and security issues.
