Distortion-Aware Adversarial Attacks on Bounding Boxes of Object Detectors Article Swipe
Phuc Van Pham
,
Son T. Vuong
,
Khang Nguyen
,
Tuan Dang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.18815
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.18815
Deep learning-based object detection has become ubiquitous in the last decade due to its high accuracy in many real-world applications. With this growing trend, these models are interested in being attacked by adversaries, with most of the results being on classifiers, which do not match the context of practical object detection. In this work, we propose a novel method to fool object detectors, expose the vulnerability of state-of-the-art detectors, and promote later works to build more robust detectors to adversarial examples. Our method aims to generate adversarial images by perturbing object confidence scores during training, which is crucial in predicting confidence for each class in the testing phase. Herein, we provide a more intuitive technique to embed additive noises based on detected objects' masks and the training loss with distortion control over the original image by leveraging the gradient of iterative images. To verify the proposed method, we perform adversarial attacks against different object detectors, including the most recent state-of-the-art models like YOLOv8, Faster R-CNN, RetinaNet, and Swin Transformer. We also evaluate our technique on MS COCO 2017 and PASCAL VOC 2012 datasets and analyze the trade-off between success attack rate and image distortion. Our experiments show that the achievable success attack rate is up to $100$\% and up to $98$\% when performing white-box and black-box attacks, respectively. The source code and relevant documentation for this work are available at the following link: https://github.com/anonymous20210106/attack_detector
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2412.18815
- https://arxiv.org/pdf/2412.18815
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405900622
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4405900622Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2412.18815Digital Object Identifier
- Title
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Distortion-Aware Adversarial Attacks on Bounding Boxes of Object DetectorsWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-12-25Full publication date if available
- Authors
-
Phuc Van Pham, Son T. Vuong, Khang Nguyen, Tuan DangList of authors in order
- Landing page
-
https://arxiv.org/abs/2412.18815Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2412.18815Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2412.18815Direct OA link when available
- Concepts
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Bounding overwatch, Adversarial system, Distortion (music), Object (grammar), Computer science, Detector, Computer security, Computer vision, Artificial intelligence, Computer network, Telecommunications, Bandwidth (computing), AmplifierTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
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.RetinaNet, | 165 |
| abstract_inverted_index.achievable | 199 |
| abstract_inverted_index.confidence | 91, 100 |
| abstract_inverted_index.detection. | 50 |
| abstract_inverted_index.detectors, | 62, 68, 154 |
| abstract_inverted_index.distortion | 129 |
| abstract_inverted_index.interested | 27 |
| abstract_inverted_index.leveraging | 136 |
| abstract_inverted_index.performing | 212 |
| abstract_inverted_index.perturbing | 89 |
| abstract_inverted_index.predicting | 99 |
| abstract_inverted_index.real-world | 18 |
| abstract_inverted_index.ubiquitous | 6 |
| abstract_inverted_index.adversarial | 79, 86, 149 |
| abstract_inverted_index.distortion. | 193 |
| abstract_inverted_index.experiments | 195 |
| abstract_inverted_index.Transformer. | 168 |
| abstract_inverted_index.adversaries, | 32 |
| abstract_inverted_index.classifiers, | 40 |
| abstract_inverted_index.applications. | 19 |
| abstract_inverted_index.documentation | 223 |
| abstract_inverted_index.respectively. | 217 |
| abstract_inverted_index.vulnerability | 65 |
| abstract_inverted_index.learning-based | 1 |
| abstract_inverted_index.state-of-the-art | 67, 159 |
| abstract_inverted_index.https://github.com/anonymous20210106/attack_detector | 233 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |