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Adversarial Examples (AEs) can deceive Deep Neural Networks (DNNs) and have received a lot of attention recently.
Y. Zhao, H. Zhu, R. Liang, Q. Shen, S. Zhang, and K. Chen, “Seeing isn’t believing: Towards more robust adversarial attack against real world object detectors,” in
2004
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L. Bottou, “Large-scale machine learning with stochastic gradient descent,” in
2010
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2013
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I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,”
2014
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S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,”
2015
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K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,”
2015
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2015
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N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” in
2016
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A. Kurakin, I. Goodfellow, S. Bengio
2016
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J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in
2016
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Z. Zhu, D. Liang, S. Zhang, X. Huang, B. Li, and S. Hu, “Traffic-sign detection and classification in the wild,” in
2016
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W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in
2016
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N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami, “Distillation as a defense to adversarial perturbations against deep neural networks,” in
2016
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N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in
2017
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2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in
2017
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2017
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T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
X. Li and F. Li, “Adversarial examples detection in deep networks with convolutional filter statistics,” in
2017
Cited alongside, same era.
J. Lu, T. Issaranon, and D. Forsyth, “Safetynet: Detecting and rejecting adversarial examples robustly,” in
2017
Cited alongside, same era.
J. H. Metzen, T. Genewein, V. Fischer, and B. Bischoff, “On detecting adversarial perturbations,”
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
X. Zhou, D. Wang, and P. Krähenbühl, “Objects as points,”
2019
Later among the works it cites.
2019
Later among the works it cites.
C. Xie, Y. Wu, L. v. d. Maaten, A. L. Yuille, and K. He, “Feature denoising for improving adversarial robustness,” in
2019
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R. Singh, A. Agarwal, M. Singh, S. Nagpal, and M. Vatsa, “On the robustness of face recognition algorithms against attacks and bias,” in
2020
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R. J. S. Raj, S. J. Shobana, I. V. Pustokhina, D. A. Pustokhin, D. Gupta, and K. Shankar, “Optimal feature selection-based medical image classification using deep learning model in internet of medical things,”
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Cited alongside, same era.
2017
Cited alongside, same era.
J. Kos, I. Fischer, and D. Song, “Adversarial examples for generative models,” in
2018
Cited alongside, same era.
A. Athalye, L. Engstrom, A. Ilyas, and K. Kwok, “Synthesizing robust adversarial examples,” in
2018
Cited alongside, same era.
2018
Cited alongside, same era.
J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,”
2018
Cited alongside, same era.
S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, “Path aggregation network for instance segmentation,” in
2018
Cited alongside, same era.
S.-T. Chen, C. Cornelius, J. Martin, and D. H. P. Chau, “Shapeshifter: Robust physical adversarial attack on faster r-cnn object detector,” in
2018
Cited alongside, same era.
2020
Later among the works it cites.
S. Lan, Z. Ren, Y. Wu, L. S. Davis, and G. Hua, “Saccadenet: A fast and accurate object detector,” in
2020
Later among the works it cites.
Y. Jin, Y. Fu, W. Wang, J. Guo, C. Ren, and X. Xiang, “Multi-feature fusion and enhancement single shot detector for traffic sign recognition,”
2020
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2020
Later among the works it cites.
Ultralytics, “yolov5,” [EB/OL],
2020
Later among the works it cites.
C.-Y. Wang, H.-Y. M. Liao, Y.-H. Wu, P.-Y. Chen, J.-W. Hsieh, and I.-H. Yeh, “Cspnet: A new backbone that can enhance learning capability of cnn,” in
2020
Later among the works it cites.
J. Li, Y. Liu, T. Chen, Z. Xiao, Z. Li, and J. Wang, “Adversarial attacks and defenses on cyber–physical systems: A survey,”
2020
Later among the works it cites.
S. Chen, N. Carlini, and D. Wagner, “Stateful detection of black-box adversarial attacks,” in
2020
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E. Wong, L. Rice, and J. Z. Kolter, “Fast is better than free: Revisiting adversarial training,”
2020
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M. Goldblum, L. Fowl, S. Feizi, and T. Goldstein, “Adversarially robust distillation,” in
2020
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Z. Wang, Y. Wu, L. Yang, A. Thirunavukarasu, C. Evison, and Y. Zhao, “Fast personal protective equipment detection for real construction sites using deep learning approaches,”
2021
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L. Schmarje, M. Santarossa, S.-M. Schröder, and R. Koch, “A survey on semi-, self-and unsupervised learning for image classification,”
2021
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M. Carranza-García, J. Torres-Mateo, P. Lara-Benítez, and J. García-Gutiérrez, “On the performance of one-stage and two-stage object detectors in autonomous vehicles using camera data,”
2021
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Y. Cai, T. Luan, H. Gao, H. Wang, L. Chen, Y. Li, M. A. Sotelo, and Z. Li, “Yolov4-5d: An effective and efficient object detector for autonomous driving,”
2021
Later among the works it cites.
M. Xue, C. Yuan, C. He, J. Wang, and W. Liu, “Naturalae: Natural and robust physical adversarial examples for object detectors,”
2021
Later among the works it cites.
M. Devi and A. Majumder, “Side-channel attack in internet of things: a survey,” in
2021
Later among the works it cites.