Physical Attack for Stereo Matching
Authors: 
Yang Liu, Jucai Zhai, Chihao Ma, Pengcheng Zeng, Xinan Wang, Yong ZhaoAuthors Info & Claims
Yang Liu, Jucai Zhai, Chihao Ma, Pengcheng Zeng, Xinan Wang, Yong ZhaoAuthors Info & ClaimsArticle No.: 27, Pages 1 - 5
Abstract
Stereo matching networks have received extensive attention in the field of autonomous driving due to their reliability and low cost in depth estimation tasks. This is largely due to the development of deep stereo networks. However, researchers have found that deep neural networks are vulnerable to adversarial attacks, and their security is worrying. So far, physical world attacks against deep stereo networks have not been systematically studied. Therefore, we extend the physical world attack to the domain of stereo matching. We design a new patch attack method for stereo matching, the disparity map attack. Extensive experiments show that our method outperforms previously published attack methods. Our patch poses a certain threat to stereo matching networks in real-world attacks.
References
[1]
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok. 2018. Synthesizing robust adversarial examples. In International conference on machine learning. PMLR, 284–293.
[2]
Zachary Berger, Parth Agrawal, Tian Yu Liu, Stefano Soatto, and Alex Wong. 2022. Stereoscopic Universal Perturbations across Different Architectures and Datasets. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 15159–15169. https://doi.org/10.1109/CVPR52688.2022.01475
[3]
Tom B. Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer. 2017. Adversarial Patch. arXiv e-prints, Article arXiv:1712.09665 (Dec. 2017), arXiv:1712.09665 pages. arxiv:1712.09665 [cs.CV]
[4]
Jia-Ren Chang and Yong-Sheng Chen. 2018. Pyramid stereo matching network. In Proceedings of the IEEE conference on computer vision and pattern recognition. 5410–5418.
[5]
Kelvin Cheng, Tianfu Wu, and Christopher Healey. 2022. Revisiting Non-Parametric Matching Cost Volumes for Robust and Generalizable Stereo Matching. Advances in Neural Information Processing Systems (2022).
[6]
Zhiyuan Cheng, James Liang, Hongjun Choi, Guanhong Tao, Zhiwen Cao, Dongfang Liu, and Xiangyu Zhang. 2022. Physical attack on monocular depth estimation with optimal adversarial patches. arXiv preprint arXiv:2207.04718 (2022).
[7]
Vinh Quang Dinh, Phuc Hong Nguyen, and Vinh Dinh Nguyen. 2022. Feature Engineering and Deep Learning for Stereo Matching Under Adverse Driving Conditions. IEEE Transactions on Intelligent Transportation Systems 23, 7 (2022), 7855–7865. https://doi.org/10.1109/TITS.2021.3073557
[8]
Andreas Geiger, Philip Lenz, and Raquel Urtasun. 2012. Are we ready for autonomous driving? The KITTI vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition. 3354–3361. https://doi.org/10.1109/CVPR.2012.6248074
[9]
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and Harnessing Adversarial Examples. arXiv e-prints, Article arXiv:1412.6572 (Dec. 2014), arXiv:1412.6572 pages. arxiv:1412.6572 [stat.ML]
[10]
Alex Kopestinsky. 2021. 25 astonishing self-driving car statistics for 2021. https://policyadvice.net/insurance/insights/self-driving-car-statistics/.
[11]
Zhaoshuo Li, Xingtong Liu, Nathan Drenkow, Andy Ding, Francis X. Creighton, Russell H. Taylor, and Mathias Unberath. 2021. Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective With Transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 6197–6206.
[12]
Nikolaus Mayer, Eddy Ilg, Philip Häusser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. 2016. A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 4040–4048. https://doi.org/10.1109/CVPR.2016.438
[13]
Moritz Menze and Andreas Geiger. 2015. Object scene flow for autonomous vehicles. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 3061–3070. https://doi.org/10.1109/CVPR.2015.7298925
[14]
Jenny Schmalfuss, Philipp Scholze, and Andrés Bruhn. 2022. A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow. In Computer Vision – ECCV 2022, Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, and Tal Hassner (Eds.). Springer Nature Switzerland, Cham, 183–200.
[15]
Simon Schrodi, Tonmoy Saikia, and Thomas Brox. 2022. Towards Understanding Adversarial Robustness of Optical Flow Networks. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 8906–8914. https://doi.org/10.1109/CVPR52688.2022.00871
[16]
Donghua Wang, Wen Yao, Tingsong Jiang, Guijiang Tang, and Xiaoqian Chen. 2022. A Survey on Physical Adversarial Attack in Computer Vision. arXiv e-prints, Article arXiv:2209.14262 (Sept. 2022), arXiv:2209.14262 pages. arxiv:2209.14262 [cs.CV]
[17]
Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, and Kilian Weinberger. 2019. Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. In CVPR.
[18]
Felix Wimbauer, Nan Yang, Lukas von Stumberg, Niclas Zeller, and Daniel Cremers. 2021. MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 6108–6118. https://doi.org/10.1109/CVPR46437.2021.00605
[19]
Haofei Xu and Juyong Zhang. 2020. AANet: Adaptive Aggregation Network for Efficient Stereo Matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1959–1968.
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Published In
January 2024
506 pages
ISBN:9798400718199
DOI:10.1145/3653804
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Published: 01 June 2024
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CVDL 2024
CVDL 2024: The International Conference on Computer Vision and Deep Learning
January 19 - 21, 2024
Changsha, China
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