Deep learning (DL) based perception models have enabled the possibility of current autonomous driving systems (ADS). However, various studies have pointed out that the DL models inside the ADS perception modules are vulnerable to adversarial attacks which can easily manipulate these DL models’ predictions. In this paper, we propose a more practical adversarial attack against the ADS perception module. Particularly, instead of targeting one of the DL models inside the ADS perception module, we propose to use one universal patch to mislead multiple DL models inside the ADS perception module simultaneously which leads to a higher chance of system-wide malfunction. We achieve such a goal by attacking the attention of DL models as a higher level of feature representation rather than traditional gradient-based attacks. We successfully generate a universal patch containing malicious perturbations that can attract multiple victim DL models’ attention to further induce their prediction errors. We verify our attack with extensive experiments on a typical ADS perception module structure with five famous datasets and also physical world scenes1.1We release our code at https://github.com/qingjiesjtu/ATTA

Modern information and communication systems have become increasingly challenging to manage. The ubiquitous system logs contain plentiful information and are thus widely exploited as an alternative source for system management. As log files usually encompass large amounts of raw data, manually analyzing them is laborious and error-prone. Consequently, many research endeavors have been devoted to automatic log analysis. However, these works typically expect structured input and struggle with the heterogeneous nature of raw system logs. Log parsing closes this gap by converting the unstructured system logs to structured records. Many parsers were proposed during the last decades to accommodate various log analysis applications. However, due to the ample solution space and lack of systematic evaluation, it is not easy for practitioners to find ready-made solutions that fit their needs. This paper aims to provide a comprehensive survey on log parsing. We begin with an exhaustive taxonomy of existing log parsers. Then we empirically analyze the critical performance and operational features for 17 open-source solutions both quantitatively and qualitatively, and whenever applicable discuss the merits of alternative approaches. We also elaborate on future challenges and discuss the relevant research directions. We envision this survey as a helpful resource for system administrators and domain experts to choose the most desirable open-source solution or implement new ones based on application-specific requirements.