Targeted Attention Attack on Deep Learning Models in Road Sign Recognition

Yang, Xinghao; Liu, Weifeng; Zhang, Shengli; Liu, Wei; Tao, Dacheng

doi:10.1109/jiot.2020.3034899

Cited by 22 publications

(8 citation statements)

References 36 publications

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“…Nassi et al [37] used a projector to project misleading traffic signs, which caused the ADSs to recognize the deceptive signs as real. Similarly, many other researchers focused on different modules to attack, such as road sign recognition [38,39], image recognition [40,41], object detection [42,43], and traffic sign recognition [44][45][46]. Other than the adversarial attacks mentioned above, false data injection [47] and denial of service attacks [48] are well-known attacks on autonomous driving systems.…”

Section: Adversarial Attacks On Autonomous Driving Systemsmentioning

confidence: 99%

Reconstruction-Based Adversarial Attack Detection in Vision-Based Autonomous Driving Systems

Hussain,

Hong

2023

MAKE

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The perception system is a safety-critical component that directly impacts the overall safety of autonomous driving systems (ADSs). It is imperative to ensure the robustness of the deep-learning model used in the perception system. However, studies have shown that these models are highly vulnerable to the adversarial perturbation of input data. The existing works mainly focused on studying the impact of these adversarial attacks on classification rather than regression models. Therefore, this paper first introduces two generalized methods for perturbation-based attacks: (1) We used naturally occurring noises to create perturbations in the input data. (2) We introduce a modified square, HopSkipJump, and decision-based/boundary attack to attack the regression models used in ADSs. Then, we propose a deep-autoencoder-based adversarial attack detector. In addition to offline evaluation metrics (e.g., F1 score and precision, etc.), we introduce an online evaluation framework to evaluate the robustness of the model under attack. The framework considers the reconstruction loss of the deep autoencoder that validates the robustness of the models under attack in an end-to-end fashion at runtime. Our experimental results showed that the proposed adversarial attack detector could detect square, HopSkipJump, and decision-based/boundary attacks with a true positive rate (TPR) of 93%.

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Section: Adversarial Attacks On Autonomous Driving Systemsmentioning

confidence: 99%

Reconstruction-Based Adversarial Attack Detection in Vision-Based Autonomous Driving Systems

Hussain,

Hong

2023

MAKE

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“…More researchers began to use deep learning methods for traffic sign detection. Yang et al [22] used adversarial machine learning to generate adversarial examples in order to improve the detection robustness of autonomous vehicles but did not consider the effect of the environment on the detection. He et al [23] presented a traffic sign detection using CapsNet [24] based on visual inspection.…”

Section: Related Workmentioning

confidence: 99%

Traffic Sign Detection via Improved Sparse R-CNN for Autonomous Vehicles

Liang

Bao

Pan

et al. 2022

Journal of Advanced Transportation

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Traffic sign detection is an important component of autonomous vehicles. There is still a mismatch problem between the existing detection algorithm and its practical application in real traffic scenes, which is mainly due to the detection accuracy and data acquisition. To tackle this problem, this study proposed an improved sparse R-CNN that integrates coordinate attention block with ResNeSt and builds a feature pyramid to modify the backbone, which enables the extracted features to focus on important information, and improves the detection accuracy. In order to obtain more diverse data, the augmentation method used is specifically designed for complex traffic scenarios, and we also present a traffic sign dataset in this study. For on-road autonomous vehicles, we designed two modules, self-adaption augmentation (SAA) and detection time augmentation (DTA), to improve the robustness of the detection algorithm. The evaluations on traffic sign datasets and on-road testing demonstrate the accuracy and effectiveness of the proposed method.

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“…Effective identification of traffic sign shapes can solve the initial reading of traffic sign information. For the detection algorithm of road signs shape, although the recognition accuracy of traffic signs has been greatly improved after continuous improvement research, due to the complexity of the road environment, the detection results when the traffic signs face occlusion, deformation and other situations are unsatisfactory [24]. In addition, the amount of calculation required to extract the shape feature information of traffic signs is large, it increases the calculation time of the model and requires higher computing power of the machine.…”

Section: Main Problems Of Road Sign Shape Recognitionmentioning

confidence: 99%

Intelligent Recognition of Traffic Signs Based on Improved YOLO v3 Algorithm

Yang

2022

Mobile Information Systems

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In recent years, assisted driving and autonomous driving technology have been paid more attention to by the public. Road sign recognition is of great practical significance for the realization of auto-driving technology. In the actual traffic environment, the traffic signs have the problems of small detectable volume, low resolution, unclear characteristics, and easy to be disturbed by the environment. In order to better realize road traffic sign recognition, this paper improves and optimizes the YOLO v3 network derived from YOLO v3 structure algorithm, enhances the data of the traffic signs by using color enhancement and other technologies, and improves the original FPN structure of the YOLO v3 network algorithm to 52 × 52. Then, the secondary sampling output characteristic diagram 108 in the YOLO v3 network is used × 108 solutions to solve these difficulties of picture size and image distortion. Use 5, 9, and 13 fixed-size pools in front of the surface of the control architecture, then the output characteristics are associated with the original characteristics of the picture so that inputs of different sizes can obtain the same output. Finally, we use the intermediate class K algorithm to group the TT100K landmark data set, reconsider the original network parameters, and compare the TT100K data set with the small target determination algorithm, such as YOLO v3 network model and improved YOLO v3 network model. The results show that compared with the traditional YOLO v3 algorithm, the optimized YOLO v3 road sign recognition algorithm has a significant improvement in sign recognition accuracy, sign recognition speed, and learning cost. When the change of FPS is very small, the recall rate and accuracy will be greatly improved. At the same time, compared with other small target detection algorithms, the improved YOLO v3 algorithm has more accurate and faster detection accuracy.

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Targeted Attention Attack on Deep Learning Models in Road Sign Recognition

Cited by 22 publications

References 36 publications

Reconstruction-Based Adversarial Attack Detection in Vision-Based Autonomous Driving Systems

Reconstruction-Based Adversarial Attack Detection in Vision-Based Autonomous Driving Systems

Traffic Sign Detection via Improved Sparse R-CNN for Autonomous Vehicles

Intelligent Recognition of Traffic Signs Based on Improved YOLO v3 Algorithm

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