Adversarial Attacks on Event-Based Pedestrian Detectors: A Physical Approach Article Swipe
Guixu Lin
,
Muyao Niu
,
Qingtian Zhu
,
Zhengwei Yin
,
Zhuoxiao Li
,
Shengfeng He
,
Yinqiang Zheng
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v39i5.32555
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v39i5.32555
Event cameras, known for their low latency and high dynamic range, show great potential in pedestrian detection applications. However, while recent research has primarily focused on improving detection accuracy, the robustness of event-based visual models against physical adversarial attacks has received limited attention. For example, adversarial physical objects, such as specific clothing patterns or accessories, can exploit inherent vulnerabilities in these systems, leading to misdetections or misclassifications. This study is the first to explore physical adversarial attacks on event-driven pedestrian detectors, specifically investigating whether certain clothing patterns worn by pedestrians can cause these detectors to fail, effectively rendering them unable to detect the person. To address this, we developed an end-to-end adversarial framework in the digital domain, framing the design of adversarial clothing textures as a 2D texture optimization problem. By crafting an effective adversarial loss function, the framework iteratively generates optimal textures through backpropagation. Our results demonstrate that the textures identified in the digital domain possess strong adversarial properties. Furthermore, we translated these digitally optimized textures into physical clothing and tested them in real-world scenarios, successfully demonstrating that the designed textures significantly degrade the performance of event-based pedestrian detection models. This work highlights the vulnerability of such models to physical adversarial attacks.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v39i5.32555
- https://ojs.aaai.org/index.php/AAAI/article/download/32555/34710
- OA Status
- diamond
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409367032
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409367032Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v39i5.32555Digital Object Identifier
- Title
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Adversarial Attacks on Event-Based Pedestrian Detectors: A Physical ApproachWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-11Full publication date if available
- Authors
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Guixu Lin, Muyao Niu, Qingtian Zhu, Zhengwei Yin, Zhuoxiao Li, Shengfeng He, Yinqiang ZhengList of authors in order
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https://doi.org/10.1609/aaai.v39i5.32555Publisher landing page
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https://ojs.aaai.org/index.php/AAAI/article/download/32555/34710Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://ojs.aaai.org/index.php/AAAI/article/download/32555/34710Direct OA link when available
- Concepts
-
Pedestrian, Adversarial system, Event (particle physics), Computer science, Detector, Computer security, Artificial intelligence, Engineering, Physics, Telecommunications, Transport engineering, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.demonstrate | 147 |
| abstract_inverted_index.effectively | 96 |
| abstract_inverted_index.event-based | 32, 187 |
| abstract_inverted_index.iteratively | 139 |
| abstract_inverted_index.pedestrians | 89 |
| abstract_inverted_index.performance | 185 |
| abstract_inverted_index.properties. | 159 |
| abstract_inverted_index.Furthermore, | 160 |
| abstract_inverted_index.accessories, | 54 |
| abstract_inverted_index.event-driven | 78 |
| abstract_inverted_index.optimization | 128 |
| abstract_inverted_index.specifically | 81 |
| abstract_inverted_index.successfully | 176 |
| abstract_inverted_index.applications. | 17 |
| abstract_inverted_index.demonstrating | 177 |
| abstract_inverted_index.investigating | 82 |
| abstract_inverted_index.misdetections | 64 |
| abstract_inverted_index.significantly | 182 |
| abstract_inverted_index.vulnerability | 195 |
| abstract_inverted_index.vulnerabilities | 58 |
| abstract_inverted_index.backpropagation. | 144 |
| abstract_inverted_index.misclassifications. | 66 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.39045226 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |