Research on video adversarial attack with long living cycle

Zeyu Zhao, Ke Xu, Xinghao Jiang, Tanfeng Sun
Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, PMLR 180:2374-2382, 2022.

Abstract

In recent years, the vulnerability of networks has attracted the attention of researchers. However, in these methods, the impact of video compression coding on the added adversarial perturbation, i.e., the robustness of the video adversarial example, is not considered. When an adversarial sample is just generated, its attack capability is the strongest. However, with multiple video encoding and video decoding in Internet transmission, the added adversarial disturbance will be continuously eliminated, eventually leading to the attack on the adversarial sample performance disappearing. We define this phenomenon as the decay of the lifetime of adversarial examples. We propose an adversarial attack method based on optimized integer space to resist this performance degradation. The robustness of anti-coding, the visual concealment, and the attack success rate are all considered during the attack process. In addition, we have also reduced the rounding loss caused by normalization in the deep neural network model process. The contributions of our methods are 1) We show the performance degradation caused by video compression coding on existing video adversarial attack methods, which seems an effective way for detecting of defending video adversarial examples. 2) A robust video adversarial attack method is proposed to resist video compression coding. The experiment shows that our method performs better on the robustness of anti-coding, visual concealment, and attack success rate.

Cite this Paper

BibTeX
@InProceedings{pmlr-v180-zhao22a, title = {Research on video adversarial attack with long living cycle}, author = {Zhao, Zeyu and Xu, Ke and Jiang, Xinghao and Sun, Tanfeng}, booktitle = {Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence}, pages = {2374--2382}, year = {2022}, editor = {Cussens, James and Zhang, Kun}, volume = {180}, series = {Proceedings of Machine Learning Research}, month = {01--05 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v180/zhao22a/zhao22a.pdf}, url = {https://proceedings.mlr.press/v180/zhao22a.html}, abstract = {In recent years, the vulnerability of networks has attracted the attention of researchers. However, in these methods, the impact of video compression coding on the added adversarial perturbation, i.e., the robustness of the video adversarial example, is not considered. When an adversarial sample is just generated, its attack capability is the strongest. However, with multiple video encoding and video decoding in Internet transmission, the added adversarial disturbance will be continuously eliminated, eventually leading to the attack on the adversarial sample performance disappearing. We define this phenomenon as the decay of the lifetime of adversarial examples. We propose an adversarial attack method based on optimized integer space to resist this performance degradation. The robustness of anti-coding, the visual concealment, and the attack success rate are all considered during the attack process. In addition, we have also reduced the rounding loss caused by normalization in the deep neural network model process. The contributions of our methods are 1) We show the performance degradation caused by video compression coding on existing video adversarial attack methods, which seems an effective way for detecting of defending video adversarial examples. 2) A robust video adversarial attack method is proposed to resist video compression coding. The experiment shows that our method performs better on the robustness of anti-coding, visual concealment, and attack success rate.} }
Endnote
%0 Conference Paper %T Research on video adversarial attack with long living cycle %A Zeyu Zhao %A Ke Xu %A Xinghao Jiang %A Tanfeng Sun %B Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2022 %E James Cussens %E Kun Zhang %F pmlr-v180-zhao22a %I PMLR %P 2374--2382 %U https://proceedings.mlr.press/v180/zhao22a.html %V 180 %X In recent years, the vulnerability of networks has attracted the attention of researchers. However, in these methods, the impact of video compression coding on the added adversarial perturbation, i.e., the robustness of the video adversarial example, is not considered. When an adversarial sample is just generated, its attack capability is the strongest. However, with multiple video encoding and video decoding in Internet transmission, the added adversarial disturbance will be continuously eliminated, eventually leading to the attack on the adversarial sample performance disappearing. We define this phenomenon as the decay of the lifetime of adversarial examples. We propose an adversarial attack method based on optimized integer space to resist this performance degradation. The robustness of anti-coding, the visual concealment, and the attack success rate are all considered during the attack process. In addition, we have also reduced the rounding loss caused by normalization in the deep neural network model process. The contributions of our methods are 1) We show the performance degradation caused by video compression coding on existing video adversarial attack methods, which seems an effective way for detecting of defending video adversarial examples. 2) A robust video adversarial attack method is proposed to resist video compression coding. The experiment shows that our method performs better on the robustness of anti-coding, visual concealment, and attack success rate.
APA
Zhao, Z., Xu, K., Jiang, X. & Sun, T.. (2022). Research on video adversarial attack with long living cycle. Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 180:2374-2382 Available from https://proceedings.mlr.press/v180/zhao22a.html.

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