Priest: Adversarial Attack Detection Techniques for Signal Injection Attacks
Machine learning is widely used for autonomous driving because it can recognize surrounding circumstances feasibly from sensor and determine appropriate actions. Most of these sensors are based on micro-electro-mechanical systems (MEMS), which enable autonomous vehicles to judge objects in conjuncti...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10225520/ |
| _version_ | 1827858033906024448 |
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| author | Jaehwan Park Changhee Hahn |
| author_facet | Jaehwan Park Changhee Hahn |
| author_sort | Jaehwan Park |
| collection | DOAJ |
| description | Machine learning is widely used for autonomous driving because it can recognize surrounding circumstances feasibly from sensor and determine appropriate actions. Most of these sensors are based on micro-electro-mechanical systems (MEMS), which enable autonomous vehicles to judge objects in conjunction with object-detection algorithms. However, recent studies have shown that MEMS are vulnerable to signal-injection attacks, in which the input images are manipulated to force the object detection algorithms to misclassify the results. These attacks can be critical in the wild because they deteriorate state-of-the-art detection techniques, dropping their detection rates until the objects would no longer be detected at all. In this paper, we propose Priest, a novel detection method against prior signal-injection attacks. Priest uses the similarity of pixel values between two consecutive images. Using only two images ensures a low computational cost. According to our performance analysis, Priest detects state-of-the-art signal-injection attacks in real-time with 99% accuracy on average, achieving practical autonomous driving security. |
| first_indexed | 2024-03-12T12:50:03Z |
| format | Article |
| id | doaj.art-e402433c413e414ca37cd17a28c00dc0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T12:50:03Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e402433c413e414ca37cd17a28c00dc02023-08-28T23:00:23ZengIEEEIEEE Access2169-35362023-01-0111894098942210.1109/ACCESS.2023.330713310225520Priest: Adversarial Attack Detection Techniques for Signal Injection AttacksJaehwan Park0https://orcid.org/0009-0002-4124-2164Changhee Hahn1https://orcid.org/0000-0003-4334-0411Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul, South KoreaDepartment of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul, South KoreaMachine learning is widely used for autonomous driving because it can recognize surrounding circumstances feasibly from sensor and determine appropriate actions. Most of these sensors are based on micro-electro-mechanical systems (MEMS), which enable autonomous vehicles to judge objects in conjunction with object-detection algorithms. However, recent studies have shown that MEMS are vulnerable to signal-injection attacks, in which the input images are manipulated to force the object detection algorithms to misclassify the results. These attacks can be critical in the wild because they deteriorate state-of-the-art detection techniques, dropping their detection rates until the objects would no longer be detected at all. In this paper, we propose Priest, a novel detection method against prior signal-injection attacks. Priest uses the similarity of pixel values between two consecutive images. Using only two images ensures a low computational cost. According to our performance analysis, Priest detects state-of-the-art signal-injection attacks in real-time with 99% accuracy on average, achieving practical autonomous driving security.https://ieeexplore.ieee.org/document/10225520/Autonomous vehicleadversarial attack detection |
| spellingShingle | Jaehwan Park Changhee Hahn Priest: Adversarial Attack Detection Techniques for Signal Injection Attacks IEEE Access Autonomous vehicle adversarial attack detection |
| title | Priest: Adversarial Attack Detection Techniques for Signal Injection Attacks |
| title_full | Priest: Adversarial Attack Detection Techniques for Signal Injection Attacks |
| title_fullStr | Priest: Adversarial Attack Detection Techniques for Signal Injection Attacks |
| title_full_unstemmed | Priest: Adversarial Attack Detection Techniques for Signal Injection Attacks |
| title_short | Priest: Adversarial Attack Detection Techniques for Signal Injection Attacks |
| title_sort | priest adversarial attack detection techniques for signal injection attacks |
| topic | Autonomous vehicle adversarial attack detection |
| url | https://ieeexplore.ieee.org/document/10225520/ |
| work_keys_str_mv | AT jaehwanpark priestadversarialattackdetectiontechniquesforsignalinjectionattacks AT changheehahn priestadversarialattackdetectiontechniquesforsignalinjectionattacks |