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There have been many efforts in attacking image classification models with adversarial perturbations, but the same topic on video classification has not yet been thoroughly studied.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li · 2009
Earlier work this paper cites.
HMDB: a large video database for human motion recognition
Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso Poggio, and Thomas Serre · 2011
Earlier work this paper cites.
UCF101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Long-term recurrent convolutional networks for visual recognition and description
Jeff Donahue, Lisa Anne Hendricks, Marcus Rohrbach, Subhashini Venugopalan, Sergio Guadarrama, Kate Saenko, and Trevor Darrell · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Adversarial manipulation of deep representations
Sara Sabour, Yanshuai Cao, Fartash Faghri, and David J Fleet · 2015
Earlier work this paper cites.
Learning spatio-temporal features with 3D convolutional networks
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Earlier work this paper cites.
Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2016
Cited alongside, same era.
Deepfool: A simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
Cited alongside, same era.
The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Cited alongside, same era.
Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
Cited alongside, same era.
Adversarial examples for semantic segmentation and object detection
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, and Alan Yuille · 2017
Later among the works it cites.
Fooling vision and language models despite localization and attention mechanism
Xiaojun Xu, Xinyun Chen, Chang Liu, Anna Rohrbach, Trevor Darrell, and Dawn Song · 2017
Later among the works it cites.
Can spatiotemporal 3D CNNs retrace the history of 2D CNNs and ImageNet?
Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh · 2018
Later among the works it cites.
Stealthy adversarial perturbations against real-time video classification systems
Shasha Li, Ajaya Neupane, Sujoy Paul, Chengyu Song, Srikanth V. Krishnamurthy, Amit K. Roy-Chowdhury, and Ananthram Swami · 2018
Later among the works it cites.
A closer look at spatiotemporal convolutions for action recognition
Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, and Manohar Paluri · 2018
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Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, and Cho-Jui Hsieh · 2017
Cited alongside, same era.
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Cited alongside, same era.
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
Cited alongside, same era.
Learning spatiotemporal representation with pseudo-3d residual networks
Zhaofan Qiu, Ting Yao, and Tao Mei · 2017
Cited alongside, same era.
Later among the works it cites.
Sparse adversarial perturbations for videos
Xingxing Wei, Jun Zhu, and Hang Su · 2018
Later among the works it cites.
Hopskipjumpattack: A query-efficient decision-based attack
Jianbo Chen, Michael I. Jordan, and Martin J. Wainwright · 2019
Closest in time.
Evaluating robustness of deep image super-resolution against adversarial attacks
Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, and Jong-Seok Lee · 2019
Closest in time.
Black-box adversarial attacks on video recognition models
Linxi Jiang, Xingjun Ma, Shaoxiang Chen, James Bailey, and Yu-Gang Jiang · 2019
Closest in time.