DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems

Zhou, Husheng; Li, Wei; Zhu, Yuankun; Zhang, Yuqun; Yu, Bei; Zhang, Lingming; Liu, Cong

doi:10.48550/arxiv.1812.10812

Cited by 11 publications

(11 citation statements)

References 29 publications

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“…Both the attacks were studied concerning different functional modules needed in vision-based autonomous driving. For example, the perturbation attack was studied regarding sign classification in [7], 2D object detection in [8], semantic segmentation in [9], [24], and monocular depth estimation in [12], [13], while the patch attack was studied regarding lane keeping in [5], optical flow estimation in [6], 2D object detection in [10], [11], and monocular depth estimation in [13]. None of these studies, however, focus directly on the attacks' impact on driving behavior and driving safety of autonomous vehicles.…”

Section: Related Workmentioning

confidence: 99%

“…Environment perception and other tasks of autonomous driving systems heavily rely on deep learning models. Researchers have demonstrated that adversarial examples, which are originally designed to affect general-purpose deep learning models, can also be used to cause malfunctions in autonomous driving tasks [5]- [14]. In these studies, researchers usually use the decline of accuracy, or the erroneous rate increase of the deep learning models, to measure the effectiveness of attacks.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles

Zhang

Lou

Wang

et al. 2022

IEEE Internet Things J.

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In recent years, many deep learning models have been adopted in autonomous driving. At the same time, these models introduce new vulnerabilities that may compromise the safety of autonomous vehicles. Specifically, recent studies have demonstrated that adversarial attacks can cause a significant decline in detection precision of deep learning-based 3D object detection models. Although driving safety is the ultimate concern for autonomous driving, there is no comprehensive study on the linkage between the performance of deep learning models and the driving safety of autonomous vehicles under adversarial attacks. In this paper, we investigate the impact of two primary types of adversarial attacks, perturbation attacks and patch attacks, on the driving safety of vision-based autonomous vehicles rather than the detection precision of deep learning models. In particular, we consider two state-of-the-art models in visionbased 3D object detection, Stereo R-CNN and DSGN. To evaluate driving safety, we propose an end-to-end evaluation framework with a set of driving safety performance metrics. By analyzing the results of our extensive evaluation experiments, we find that (1) the attack's impact on the driving safety of autonomous vehicles and the attack's impact on the precision of 3D object detectors are decoupled, and (2) the DSGN model demonstrates stronger robustness to adversarial attacks than the Stereo R-CNN model. In addition, we further investigate the causes behind the two findings with an ablation study. The findings of this paper provide a new perspective to evaluate adversarial attacks and guide the selection of deep learning models in autonomous driving.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles

Zhang

Lou

Wang

et al. 2022

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…[219]), adversarial testing inputs (e.g. [53,217,228]) or increase the coverage of the test suites (e.g. [49,126]).…”

Section: Software Testing (115 Studies)mentioning

confidence: 99%

Software Engineering for AI-Based Systems: A Survey

Martínez-Fernández,

Bogner,

Franch

et al. 2021

Preprint

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AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-and speechrecognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems.To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula. CCS Concepts: • Software and its engineering → Software creation and management; • Computing methodologies → Machine learning;

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“…Different from these techniques, D2C uses sequential pattern mining to aid the extraction of constraints from API documents to guide input generation for testing DL libraries. Testing DL models: Many fuzzing techniques test the robustness of DL models instead of DL libraries by finding adversarial inputs (e.g., images or natural language texts) for the models [23,28,31,39,43,44,60,63,64,66,68,71,77,79]. Testing DL models alone is insufficient, as DL libraries contain bugs [32,33,51,73,74], which hurt the accuracy and speed of the entire DL system [51].…”

Section: Related Workmentioning

confidence: 99%

DocTer: Documentation Guided Fuzzing for Testing Deep Learning API Functions

Xie,

Li,

Kim

et al. 2021

Preprint

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It is integral to test API functions of widely-used deep learning (DL) libraries. The effectiveness of such testing requires DL-specific input constraints of these API functions. Such constraints enable the generation of valid inputs, i.e., inputs that follow these DL-specific constraints, to explore deep to test the core functionality of API functions. Existing fuzzers have no knowledge of such constraints, and existing constraint-extraction techniques are ineffective for extracting DL-specific input constraints.To fill this gap, we design and implement a document-guided fuzzing technique-D2C-for API functions of DL libraries. D2C leverages sequential pattern mining to generate rules for extracting DL-specific constraints from API documents and uses these constraints to guide the fuzzing to generate valid inputs automatically. D2C also generates inputs that violate these constraints to test the input validity checking code. In addition, D2C uses the constraints to generate boundary inputs to detect more bugs.Our evaluation of three popular DL libraries (TensorFlow, Py-Torch, and MXNet) shows that D2C's accuracy in extracting input constraints is 83.3-90.0%. D2C detects 121 bugs, while a baseline fuzzer without input constraints detects only 68 bugs. Most (89) of the 121 bugs are previously unknown, 54 of which have been fixed or confirmed by developers after we report them. In addition, D2C detects 38 inconsistencies within documents, including 28 that are fixed or confirmed after we report them.

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DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems

Cited by 11 publications

References 29 publications

Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles

Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles

Software Engineering for AI-Based Systems: A Survey

DocTer: Documentation Guided Fuzzing for Testing Deep Learning API Functions

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