Review of Adversarial Attacks in Object Detection

Research Article
Open access

Review of Adversarial Attacks in Object Detection

Li Wang 1*
  • 1 University of British Columbia    
  • *corresponding author liwang955@outlook.com
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/19/20231029
ACE Vol.19
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-029-5
ISBN (Online): 978-1-83558-030-1

Abstract

Object detection, a fundamental element of computer vision and artificial intelligence, has experienced considerable advancements through the incorporation of deep learning-based techniques. Yet, despite the impressive strides in both accuracy and efficiency, object detection algorithms harbor inherent vulnerabilities to adversarial attacks. These well-crafted disruptions pose significant risks, especially considering the broad application of object detection across an array of safety-critical sectors such as autonomous transportation, medical imaging, and security systems. This comprehensive paper offers a thorough review of adversarial attacks against object detection systems, dissecting the methods employed, and scrutinizing the implications of their exploits. It dives deep into the mechanics and consequences of both white-box and black-box attacks on prevalent object detection networks, including but not limited to Faster R-CNN, YOLO, and SSD. Furthermore, this paper underscores an assortment of defense strategies developed to mitigate the effects of adversarial attacks. These include adversarial training, gradient masking, input transformations, and randomized defenses. While these strategies hold promise, it is acknowledged that they have their limitations and do not offer a universal solution against all adversarial attacks. As such, this paper underscores the urgent necessity for robust defense mechanisms and stimulates further discourse and investigation into developing truly resilient object detection systems, capable of withstanding the growing threat of adversarial attacks.

Keywords:

Adversarial Attack, Object Detection, Deep Learning

Wang,L. (2023). Review of Adversarial Attacks in Object Detection. Applied and Computational Engineering,19,178-183.
Export citation

References

[1]. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587).

[2]. Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems (pp. 91-99).

[3]. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).

[4]. Zou, X. (2019). A Review of Object Detection Techniques. In 2019 International Conference on Smart Grid and Electrical Automation (ICSGEA) (pp. 251-254). Xiangtan, China. https://doi.org/10.1109/ICSGEA.2019.00065.

[5]. Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572.

[6]. Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017). Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083.

[7]. Chakraborty, A., Alam, M., Dey, V., Chattopadhyay, A., & Mukhopadhyay, D. (2018). Adversarial Attacks and Defences: A Survey. ArXiv. /abs/1810.00069

[8]. Carlini, N., & Wagner, D. (2017). Towards evaluating the robustness of neural networks. In 2017 IEEE Symposium on Security and Privacy (SP) (pp. 39-57). IEEE.

[9]. Al-Shaer, R., Spring, J. M., & Christou, E. (2020). Learning the associations of MITRE ATT & CK Adversarial Techniques. In 2020 IEEE Conference on Communications and Network Security (CNS) (pp. 1-9). IEEE. https://doi.org/10.1109/cns48642.2020.9162207

[10]. Qiu, S., Liu, Q., Zhou, S., & Wu, C. (2019). Review of Artificial Intelligence Adversarial Attack and Defense Technologies. Applied Sciences, 9(5), 909. MDPI AG. Retrieved from http://dx.doi.org/10.3390/app9050909

[11]. Girshick, R. (2015). Fast R-CNN. In Proceedings of the IEEE international conference on computer vision (pp. 1440-1448).

[12]. Lu, X., Li, Q., Li, B., Yan, J. (2020). MimicDet: Bridging the Gap Between One-Stage and Two-Stage Object Detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12359. Springer, Cham. https://doi.org/10.1007/978-3-030-58568-6_32

[13]. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. In European conference on computer vision (pp. 21-37). Springer, Cham.

[14]. Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980-2988).

[15]. Chen, P. Y., Zhang, H., Sharma, Y., Yi, J., & Hsieh, C. J. (2017). Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models. In Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security (pp. 15-26).

[16]. Papernot, N., McDaniel, P., Wu, X., Jha, S., & Swami, A. (2016). Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks. In 2016 IEEE Symposium on Security and Privacy (SP) (pp. 582-597). San Jose, CA, USA. https://doi.org/10.1109/SP.2016.41.

Cite this article

Wang,L. (2023). Review of Adversarial Attacks in Object Detection. Applied and Computational Engineering,19,178-183.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-029-5(Print) / 978-1-83558-030-1(Online)
Editor：Roman Bauer, Marwan Omar, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.19
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587).

[2]. Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems (pp. 91-99).

[3]. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).

[4]. Zou, X. (2019). A Review of Object Detection Techniques. In 2019 International Conference on Smart Grid and Electrical Automation (ICSGEA) (pp. 251-254). Xiangtan, China. https://doi.org/10.1109/ICSGEA.2019.00065.

[5]. Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572.

[6]. Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017). Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083.

[7]. Chakraborty, A., Alam, M., Dey, V., Chattopadhyay, A., & Mukhopadhyay, D. (2018). Adversarial Attacks and Defences: A Survey. ArXiv. /abs/1810.00069

[8]. Carlini, N., & Wagner, D. (2017). Towards evaluating the robustness of neural networks. In 2017 IEEE Symposium on Security and Privacy (SP) (pp. 39-57). IEEE.

[9]. Al-Shaer, R., Spring, J. M., & Christou, E. (2020). Learning the associations of MITRE ATT & CK Adversarial Techniques. In 2020 IEEE Conference on Communications and Network Security (CNS) (pp. 1-9). IEEE. https://doi.org/10.1109/cns48642.2020.9162207

[10]. Qiu, S., Liu, Q., Zhou, S., & Wu, C. (2019). Review of Artificial Intelligence Adversarial Attack and Defense Technologies. Applied Sciences, 9(5), 909. MDPI AG. Retrieved from http://dx.doi.org/10.3390/app9050909

[11]. Girshick, R. (2015). Fast R-CNN. In Proceedings of the IEEE international conference on computer vision (pp. 1440-1448).

[12]. Lu, X., Li, Q., Li, B., Yan, J. (2020). MimicDet: Bridging the Gap Between One-Stage and Two-Stage Object Detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12359. Springer, Cham. https://doi.org/10.1007/978-3-030-58568-6_32

[13]. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. In European conference on computer vision (pp. 21-37). Springer, Cham.

[14]. Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980-2988).

[15]. Chen, P. Y., Zhang, H., Sharma, Y., Yi, J., & Hsieh, C. J. (2017). Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models. In Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security (pp. 15-26).

[16]. Papernot, N., McDaniel, P., Wu, X., Jha, S., & Swami, A. (2016). Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks. In 2016 IEEE Symposium on Security and Privacy (SP) (pp. 582-597). San Jose, CA, USA. https://doi.org/10.1109/SP.2016.41.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.