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Video classification systems are vulnerable to adversarial attacks, which can create severe security problems in video verification.
R. Likert, “A technique for the measurement of attitudes.” Archives of Psychology , 1932
1932
Earlier work this paper cites.
H. Mann and W. D.R., “On a test of whether one of two random variables is stochastically larger than the other,” The Annals of Mathematical Statistics , 1947
1947
Earlier work this paper cites.
A. R. Smith, “Color gamut transform pairs,” ACM Siggraph Computer Graphics , vol. 12, no. 3, pp. 12–19, 1978
1978
Earlier work this paper cites.
C. M. Stein, “Estimation of the mean of a multivariate normal distribution,” The Annals of Statistics , pp. 1135–1151, 1981
1981
Earlier work this paper cites.
P. Heckbert, “Color image quantization for frame buffer display,” ACM Siggraph Computer Graphics , vol. 16, no. 3, pp. 297–307, 1982
1982
Earlier work this paper cites.
D. Dowson and B. Landau, “The fréchet distance between multivariate normal distributions,” Journal of Multivariate Analysis , 1982
1982
Earlier work this paper cites.
A. Hertzmann, C. E. Jacobs, N. Oliver, B. Curless, and D. H. Salesin, “Image analogies,” in Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques , 2001, pp. 327–340
2001
Earlier work this paper cites.
Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing , 2004
2004
Earlier work this paper cites.
H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre, “Hmdb: a large video database for human motion recognition,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2011, pp. 2556–2563
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems (NeurIPS) , pp. 1097–1105, 2012
2012
Earlier work this paper cites.
S. Ji, W. Xu, M. Yang, and K. Yu, “3d convolutional neural networks for human action recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , vol. 35, no. 1, pp. 221–231, 2012
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
X. Cheng, J. Liu, and C. Dale, “Understanding the characteristics of internet short video sharing: A youtube-based measurement study,” IEEE Transactions on Multimedia , vol. 15, no. 5, 2013
2013
Earlier work this paper cites.
P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid, “Deepflow: Large displacement optical flow with deep matching,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2013, pp. 1385–1392
2013
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2014
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Two-stream convolutional networks for action recognition in videos,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 27, 2014
2014
Earlier work this paper cites.
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR) , 2014
2014
Earlier work this paper cites.
Y. Bar, N. Levy, and L. Wolf, “Classification of artistic styles using binarized features derived from a deep neural network,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2014
2014
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2015
2015
Earlier work this paper cites.
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2015, pp. 4489–4497
2015
Earlier work this paper cites.
J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell, “Long-term recurrent convolutional networks for visual recognition and description,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2015, pp. 2625–2634
2015
Earlier work this paper cites.
J. Y.-H. Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, “Beyond short snippets: Deep networks for video classification,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2015, pp. 4694–4702
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2015
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
M. Marciel, R. Cuevas, A. Banchs, R. González, S. Traverso, M. Ahmed, and A. Azcorra, “Understanding the detection of view fraud in video content portals,” in Proceedings of the 25th International Conference on World Wide Web (WWW) , 2016
2016
Earlier work this paper cites.
J. Thies, M. Zollhofer, M. Stamminger, C. Theobalt, and M. Niessner, “Face2face: Real-time face capture and reenactment of rgb videos,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 2387–2395
2016
Earlier work this paper cites.
N. Carlini, P. Mishra, T. Vaidya, Y. Zhang, M. Sherr, C. Shields, D. Wagner, and W. Zhou, “Hidden voice commands,” in Proceedings of 25th USENIX Security Symposium (USENIX Security 16) , 2016, pp. 513–530
2016
Earlier work this paper cites.
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 2574–2582
2016
Earlier work this paper cites.
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” in Proceedings of the IEEE European Symposium on Security and Privacy (EuroS&P) , 2016, pp. 372–387
2016
Cited alongside, same era.
J. Justin, A. Alexandre, and F. Li, “Perceptual losses for real-time style transfer and super-resolution,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2016
2016
Cited alongside, same era.
M. Ruder, A. Dosovitskiy, and T. Brox, “Artistic style transfer for videos,” in Proceedings of the German Conference on Pattern Recognition (GCPR) , 2016, pp. 26–36
2016
Cited alongside, same era.
J. Carreira and A. Zisserman, “Quo vadis, action recognition? a new model and the kinetics dataset,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 6299–6308
2017
Cited alongside, same era.
A. Shafahi, M. Najibi, M. A. Ghiasi, Z. Xu, J. Dickerson, C. Studer, L. S. Davis, G. Taylor, and T. Goldstein, “Adversarial training for free!” Advances in Neural Information Processing Systems (NeurIPS) , vol. 32, 2019
2019
Later among the works it cites.
S. Li, A. Neupane, S. Paul, C. Song, S. V. Krishnamurthy, A. K. Roy-Chowdhury, and A. Swami, “Stealthy adversarial perturbations against real-time video classification systems.” in Proceedings of the Symposium on Network and Distributed Systems Security (NDSS) , 2019
2019
Later among the works it cites.
L. Jiang, X. Ma, S. Chen, J. Bailey, and Y.-G. Jiang, “Black-box adversarial attacks on video recognition models,” in Proceedings of the 27th ACM International Conference on Multimedia (ACM MM) , 2019, pp. 864–872
2019
Later among the works it cites.
J. Su, D. V. Vargas, and K. Sakurai, “One pixel attack for fooling deep neural networks,” IEEE Transactions on Evolutionary Computation (TEC) , vol. 23, no. 5, pp. 828–841, 2019
2019
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N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in Proceedings of the IEEE Symposium on Security & Privacy (SP) , 2017, pp. 39–57
2017
Cited alongside, same era.
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against machine learning,” in Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security (AsiaCCS) , 2017, pp. 506–519
2017
Cited alongside, same era.
P.-Y. Chen, H. Zhang, Y. Sharma, J. Yi, and C.-J. Hsieh, “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 , 2017, pp. 15–26
2017
Cited alongside, same era.
H. Huang, H. Wang, W. Luo, L. Ma, W. Jiang, X. Zhu, Z. Li, and W. Liu, “Real-time neural style transfer for videos,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 783–791
2017
Cited alongside, same era.
Y. Li, N. Wang, J. Liu, and X. Hou, “Demystifying neural style transfer,” in Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI) , 2017, pp. 2230–2236
2017
Cited alongside, same era.
T. Sun, Y. Wang, J. Yang, and X. Hu, “Convolution neural networks with two pathways for image style recognition,” IEEE Transactions on Image Processing (TIP) , 2017
2017
Cited alongside, same era.
Q. Kong, M.-A. Rizoiu, S. Wu, and L. Xie, “Will this video go viral: Explaining and predicting the popularity of youtube videos,” in Companion Proceedings of the The Web Conference (WWW) , 2018, pp. 175–178
2018
Cited alongside, same era.
S. Chen, C. Peng, L. Cai, and L. Guo, “A deep neural network model for target-based sentiment analysis,” in 2018 International Joint Conference on Neural Networks (IJCNN) , 2018, pp. 1–7
2018
Cited alongside, same era.
Later among the works it cites.
M. Zajac, K. Zołna, N. Rostamzadeh, and P. O. Pinheiro, “Adversarial framing for image and video classification,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , 2019, pp. 10 077–10 078
2019
Later among the works it cites.
X. Wei, J. Zhu, S. Yuan, and H. Su, “Sparse adversarial perturbations for videos,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , 2019, pp. 8973–8980
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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
2019
Later among the works it cites.
G.-H. Lee, Y. Yuan, S. Chang, and T. Jaakkola, “Tight certificates of adversarial robustness for randomly smoothed classifiers,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 32, 2019
2019
Later among the works it cites.
B. Liu, M. Wu, M. Tao, Q. Wang, L. He, G. Shen, K. Chen, and J. Yan, “Video content analysis for compliance audit in finance and security industry,” IEEE Access , vol. 8, pp. 117 888–117 899, 2020
2020
Later among the works it cites.
Z. Wei, J. Chen, X. Wei, L. Jiang, T.-S. Chua, F. Zhou, and Y.-G. Jiang, “Heuristic black-box adversarial attacks on video recognition models,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , 2020, pp. 12 338–12 345
2020
Later among the works it cites.
S.-Y. Wang, O. Wang, R. Zhang, A. Owens, and A. A. Efros, “Cnn-generated images are surprisingly easy to spot… for now,” in Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR) , 2020
2020
Later among the works it cites.
A. Bhattad, M. J. Chong, K. Liang, B. Li, and D. A. Forsyth, “Unrestricted adversarial examples via semantic manipulation,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2020
2020
Later among the works it cites.
A. S. Shamsabadi, R. Sanchez-Matilla, and A. Cavallaro, “Colorfool: Semantic adversarial colorization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 1151–1160
2020
Later among the works it cites.
C. Yang, A. Kortylewski, C. Xie, Y. Cao, and A. Yuille, “Patchattack: A black-box texture-based attack with reinforcement learning,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2020
2020
Later among the works it cites.
R. Duan, X. Ma, Y. Wang, and J. Bailey, “Adversarial camouflage: Hiding physical-world attacks with natural styles,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 1000–1008
2020
Later among the works it cites.
D. Kumar, C. Kumar, C. W. Seah, S. Xia, and S. Ming, “Finding achilles’ heel: Adversarial attack on multi-modal action recognition,” in Proceedings of the 28th ACM International Conference on Multimedia (ACM MM) , 2020
2020
Later among the works it cites.
G. Yang, T. Duan, J. E. Hu, H. Salman, I. Razenshteyn, and J. Li, “Randomized smoothing of all shapes and sizes,” in Proceedings of the 37th International Conference on Machine Learning (ICML) , vol. 119, 2020, pp. 10 693–10 705
2020
Later among the works it cites.
A. Kumar, A. Levine, T. Goldstein, and S. Feizi, “Curse of dimensionality on randomized smoothing for certifiable robustness,” in Proceedings of the International Conference on Machine Learning (ICML) , 2020
2020
Later among the works it cites.
A. Blum, T. Dick, N. Manoj, and H. Zhang, “Random smoothing might be unable to certify ℓ ∞ \ell_{\infty} robustness for high-dimensional images,” Journal of Machine Learning Research (JMLR) , 2020
2020
Later among the works it cites.
S. Li, A. Aich, S. Zhu, S. Asif, C. Song, A. Roy-Chowdhury, and S. Krishnamurthy, “Adversarial attacks on black box video classifiers: Leveraging the power of geometric transformations,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 34, pp. 2085–2096, 2021
2021
Later among the works it cites.
Z. Wang, C. Sha, and S. Yang, “Reinforcement learning based sparse black-box adversarial attack on video recognition models,” in Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI) , 2021
2021
Later among the works it cites.
R. Pony, I. Naeh, and S. Mannor, “Over-the-air adversarial flickering attacks against video recognition networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021
2021
Later among the works it cites.
Z. Chen, L. Xie, S. Pang, Y. He, and Q. Tian, “Appending adversarial frames for universal video attack,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , 2021, pp. 3199–3208
2021
Later among the works it cites.
C. B. El Vaigh, N. Garcia, B. Renoust, C. Chu, Y. Nakashima, and H. Nagahara, “Gcnboost: Artwork classification by label propagation through a knowledge graph,” in Proceedings of the International Conference on Multimedia Retrieval (ICMR) , 2021
2021
Later among the works it cites.
J. Wakefield. (2022) Deepfake presidents used in Russia-Ukraine war. https://www.bbc.com/news/technology-60780142
2022
Closest in time.
S. Xie, H. Wang, Y. Kong, and Y. Hong, “Universal 3-dimensional perturbations for black-box attacks on video recognition systems,” in Proceedings of the IEEE Symposium on Security & Privacy (SP) , 2022
2022
Closest in time.
2022
Closest in time.
“Cisco annual internet report (2018–2023) white paper,” https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html
2023
Closest in time.