Q. Liu, P. Li, W. Zhao, W. Cai, S. Yu, V. C. M. Leung, A survey on security threats and defensive techniques of machine learning: a data driven view. IEEE Access. 6:, 12103–12117 (2018).
Article
Google Scholar
I. J. Goodfellow, J. Shlens, C. Szegedy, in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, ed. by Y. Bengio, Y. LeCun. Explaining and harnessing adversarial examples, (2015). http://arxiv.org/abs/1412.6572.
I. Sergeev, How do we deal with copying content, or the first adversarial attack in production (2019). https://habr.com/en/company/avito/blog/452142/.
Y. Lu, Y. Jia, J. Wang, B. Li, W. Chai, L. Carin, S. Velipasalar, in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Enhancing cross-task black-box transferability of adversarial examples with dispersion reduction, (2020), pp. 937–946. https://doi.org/10.1109/CVPR42600.2020.00102.
P. Y. Chen, H. Zhang, Y. Sharma, J. Yi, C. J. Hsieh, in Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security AISec ’17. Zoo: zeroth order optimization based black-box attacks to deep neural networks without training substitute models (Association for Computing MachineryNew York, NY, USA, 2017), pp. 15–26. https://doi.org/10.1145/3128572.3140448.
Google Scholar
A. Ilyas, L. Engstrom, A. Athalye, J. Lin, Black-box adversarial attacks with limited queries and information. CoRR. abs/1804.08598:, 3–5 (2018). http://arxiv.org/abs/1804.085981804.08598.
Google Scholar
A. N. Bhagoji, W. He, B. Li, D. Song, in Computer Vision – ECCV 2018, ed. by V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss. Practical black-box attacks on deep neural networks using efficient query mechanisms (Springer International PublishingCham, 2018), pp. 158–174.
Chapter
Google Scholar
Y. Dong, F. Liao, T. Pang, X. Hu, J. Zhu, Discovering adversarial examples with momentum. CoRR. abs/1710.06081: (2017). http://arxiv.org/abs/1710.060811710.06081.
N. Papernot, P. D. McDaniel, I. J. Goodfellow, S. Jha, Z. B. Celik, A. Swami, Practical black-box attacks against deep learning systems using adversarial examples. CoRR. abs/1602.02697:, 3–5 (2016). http://arxiv.org/abs/1602.026971602.02697.
Google Scholar
Y. Liu, X. Chen, C. Liu, D. Song, Delving into transferable adversarial examples and black-box attacks. CoRR. abs/1611.02770: (2016). http://arxiv.org/abs/1611.02770.
W. Zhou, X. Hou, Y. Chen, M. Tang, X. Huang, X. Gan, Y. Yang, in Computer Vision – ECCV 2018, ed. by V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss. Transferable adversarial perturbations (Springer International PublishingCham, 2018), pp. 471–486.
Chapter
Google Scholar
P. Guo, Y. Xu, B. Lin, Y. Zhang, Multi-task adversarial attack. CoRR. abs/2011.09824: (2020). https://arxiv.org/abs/2011.09824.
S. M. Moosavi-Dezfooli, A. Fawzi, P. Frossard, in 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016. Deepfool: a simple and accurate method to fool deep neural networks (IEEE Computer SocietyLas Vegas, NV, USA, 2016), pp. 2574–2582. https://doi.org/10.1109/CVPR.2016.282.
Google Scholar
M. Barni, K. Kallas, E. Nowroozi, B. Tondi, in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). On the transferability of adversarial examples against cnn-based image forensics, (2019). https://doi.org/10.1109/ICASSP.2019.8683772.
M. Barni, E. Nowroozi, B. Tondi, B. Zhang, in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Effectiveness of random deep feature selection for securing image manipulation detectors against adversarial examples, (2020). https://doi.org/10.1109/ICASSP40776.2020.9053318.
A. Kurakin, I. J. Goodfellow, S. Bengio, in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. Adversarial machine learning at scale (OpenReview.net, 2017). https://openreview.net/forum?id=BJm4T4Kgx.
J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, L. Fei-Fei, in IEEE Conference on Computer Vision and Pattern Recognition. Imagenet: a large-scale hierarchical image database, (2009). https://doi.org/10.1109/CVPR.2009.5206848.
T. -Y. Lin, L. M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, C. L. Zitnick, in Computer Vision – ECCV 2014, ed. by D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars. Microsoft coco: common objects in context (Springer International PublishingCham, 2014), pp. 740–755.
Chapter
Google Scholar
K. Simonyan, A. Zisserman, in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, ed. by Y. Bengio, Y. LeCun. Very deep convolutional networks for large-scale image recognition, (2015). http://arxiv.org/abs/1409.1556.
K. He, X. Zhang, S. Ren, J. Sun, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Deep residual learning for image recognition, (2016). https://doi.org/10.1109/CVPR.2016.90.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Rethinking the inception architecture for computer vision, (2016). https://doi.org/10.1109/CVPR.2016.308.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L. C. Chen, in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Mobilenetv2: inverted residuals and linear bottlenecks, (2018). https://doi.org/10.1109/CVPR.2018.00474.
R. Girshick, in IEEE International Conference on Computer Vision (ICCV). Fast r-cnn, (2015). https://doi.org/10.1109/ICCV.2015.169.
K. He, G. Gkioxari, P. Dollar, R. Girshick, in 2017 IEEE International Conference on Computer Vision (ICCV). Mask R-CNN, (2017), pp. 2980–2988. https://doi.org/10.1109/ICCV.2017.322.
J. Redmon, A. Farhadi, Yolov3: an incremental improvement (2018). http://arxiv.org/abs/1804.02767.
R. Girshick, J. Donahue, T. Darrell, J. Malik, in IEEE Conference on Computer Vision and Pattern Recognition. Rich feature hierarchies for accurate object detection and semantic segmentation, (2014). https://doi.org/10.1109/CVPR.2014.81.
L. C. Chen, G. Papandreou, F. Schroff, H. Adam, Rethinking Atrous convolution for semantic image segmentation. CoRR. abs/1706.05587: (2017). http://arxiv.org/abs/1706.05587.
L. Valerio, F. M. Nardini, A. Passarella, R. Perego, Dynamic hard pruning of neural networks at the edge of the internet. CoRR. abs/2011.08545: (2020). https://arxiv.org/abs/2011.08545.
A. Mordvintsev, C. Olah, M. Tyka, Inceptionism: going deeper into neural networks (2015). https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html.