Cameras capture images that are essential for many safety-critical tasks. To process these images, a complex pipeline with multiple layers is used. Security attacks on this pipeline can severely affect passenger safety and system performance. However, many attacks presented in scientific literature overlook the fact that there are different layers and, hence, the feasibility and impact of these attacks can vary. While there has been research to improve the quality and robustness of the image processing pipeline, these efforts are often orthogonal to security research without exploiting potential overlap and synergies. In this work, we aim to bridge this gap by combining security and robustness research for the image processing pipeline in autonomous vehicles. We thoroughly investigated the body of literature on the security and robustness of the image processing pipeline and selected 92 papers for deeper discussion in this SoK. For the security domain, we classify the risk of attacks using the automotive security standard ISO 21434, emphasizing the need to consider all layers for overall system security. With our online tool TARA-CAM, we propose an interactive method to perform threat analysis and risk assessment following the ISO standard. We also demonstrate how existing robustness research can help mitigate the impact of attacks, addressing the current research gap. Finally, we present PICT, an embedded open-source testbed that can influence various parameters across all layers, allowing researchers to analyze the effects of different defense strategies and attack impacts. With this SoK, we contribute a comprehensive discussion and systematic analysis of existing approaches to image processing pipeline security and robustness, together with an open-source tool and testbed that jointly facilitates hardening the image processing pipeline against existing and future security attacks.

翻译：摄像头捕获的图像对许多安全关键任务至关重要。为处理这些图像，需采用包含多个层级的复杂流水线。针对该流水线的安全攻击可能严重影响乘客安全与系统性能。然而，现有学术文献中的许多攻击研究忽略了不同层级的存在，导致其可行性与影响程度可能存在差异。尽管已有研究致力于提升图像处理流水线的质量与鲁棒性，但这些工作常与安全研究正交，未能充分利用潜在的交叉与协同效应。本研究旨在通过融合自动驾驶汽车图像处理流水线的安全性与鲁棒性研究来弥合这一鸿沟。我们系统梳理了图像处理流水线安全性与鲁棒性相关文献，并精选92篇论文在本SoK中进行深入探讨。在安全领域，我们依据汽车安全标准ISO 21434对攻击风险进行分类，强调需统筹考虑所有层级以实现整体系统安全。通过在线工具TARA-CAM，我们提出一种遵循ISO标准的交互式威胁分析与风险评估方法。我们还论证了现有鲁棒性研究如何助力缓解攻击影响，以应对当前研究空白。最后，我们推出PICT——一个可调节所有层级多种参数的嵌入式开源测试平台，使研究人员能够分析不同防御策略与攻击影响的效果。本SoK通过对图像处理流水线安全性与鲁棒性现有方法的全面论述与系统分析，结合开源工具与测试平台，共同为强化图像处理流水线抵御现有及未来安全攻击提供了支撑。