基于空间感知的多级损失目标跟踪对抗攻击方法

doi:10.11959/j.issn.1000-436x.2021208

Abstract

Abstract:

In order to solve the problem that it is difficult for the existing adversarial disturbance techniques to effectively reduce the discrimination ability of the trackers and make the trajectory deviation rapidly, an effective object tracking adversarial attack method was proposed.First, deception loss, drift loss and attention mechanism-based loss was designed to jointly train generator based on the consideration of the high-level categories and the low-level features.Then, the clean image was sent to the trained generator to generate the adversarial samples that were used to interfere with the object trackers, which made the object trajectory deviation and reduced the tracking accuracy.Experimental results show that the proposed method achieves 54% reduction in success rate and 70% reduction in accuracy on OTB dataset, which can attack the object of tracking quickly in complex scenes.

Key words: video surveillance, network security, adversarial attack, deep learning, object tracking

CLC Number:

TP391

Xu CHENG, Yingying WANG, Nianjie ZHANG, Zhangjie FU, Beijing CHEN, Guoying ZHAO. Multi-level loss object tracking adversarial attack method based on spatial perception[J]. Journal on Communications, 2021, 42(11): 242-254.

Figures/Tables 14

References 42

[1]	BOLME D S , BEVERIDGE J R , DRAPER B A ,et al. Visual object tracking using adaptive correlation filters[C]// Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2010: 2544-2550.
[2]	HENRIQUES J F , CASEIRO R , MARTINS P ,et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015,37(3): 583-596.
[3]	DANELLJAN M , H?GER G , SHAHBAZ K F ,et al. Accurate scale estimation for robust visual tracking[C]// Proceedings of Proceedings of the British Machine Vision Conference 2014.[S.n.:s.l.], 2014: 1-11.
[4]	LI Y , ZHU J K , HOI S C H . Reliable patch trackers:robust visual tracking by exploiting reliable patches[C]// Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2015: 353-361.
[5]	DANELLJAN M , H?GER G , KHAN F S ,et al. Learning spatially regularized correlation filters for visual tracking[C]// Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV). Piscataway:IEEE Press, 2015: 4310-4318.
[6]	DANELLJAN M , ROBINSON A , SHAHBAZ K ,et al. Beyond correlation filters:learning continuous convolution operators for visual tracking[C]// Proceedings of the European Conference on Computer Vision. Berlin:Springer, 2016: 472-488.
[7]	WANG N , YEUNG D Y . Learning a deep compact image representation for visual tracking[C]// Proceedings of the Annual Conference on Neural Information Processing Systems. New York:Curran Associates, 2013: 809-817.
[8]	HONG S , YOU T , KWAK S ,et al. Online tracking by learning discriminative saliency map with convolutional neural network[C]// Proceedings of the International Conference on Machine Learning. New York:ACM Press, 2015: 597-606.
[9]	WANG L J , OUYANG W L , WANG X G ,et al. Visual tracking with fully convolutional networks[C]// Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV). Piscataway:IEEE Press, 2015: 3119-3127.
[10]	MA C , HUANG J B , YANG X K ,et al. Hierarchical convolutional features for visual tracking[C]// Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV). Piscataway:IEEE Press, 2015: 3074-3082.
[11]	SONG Y B , MA C , WU X H ,et al. VITAL:Visual tracking via adversarial learning[C]// Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2018: 8990-8999.
[12]	NAM H , HAN B . Learning multi-domain convolutional neural networks for visual tracking[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2016: 4293-4302.
[13]	BERTINETTO L , VALMADRE J , HENRIQUES J F ,et al. Fully-convolutional Siamese networks for object tracking[C]// Proceedings of the European Conference on Computer Vision. Berlin:Springer, 2016: 850-865.
[14]	LI B , YAN J J , WU W ,et al. High performance visual tracking with Siamese region proposal network[C]// Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2018: 8971-8980.
[15]	LI B , WU W , WANG Q ,et al. SiamRPN++:evolution of Siamese visual tracking with very deep networks[C]// Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2019: 4282-4291.
[16]	ZHU Z , WANG Q , LI B ,et al. Distractor-aware Siamese networks for visual object tracking[C]// Proceedings of the European Conference on Computer Vision. Berlin:Springer, 2018: 101-117.
[17]	VOIGTLAENDER P , LUITEN J , TORR P H S ,et al. Siam R-CNN:visual tracking by Re-detection[C]// Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2020: 6577-6587.
[18]	GOODFELLOW I J , SHLENS J , SZEGEDY C . Explaining and harnessing adversarial examples[C]// Proceedings of the third International Conference on Learning Representations. Piscataway:IEEE Press, 2015: 1-11.
[19]	SEYED M , MOOSAVI D , ALHUSSEIN F ,et al. Deepfool:a simple and accurate method to fool deep neural networks[C]// Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2016: 2574-2582.
[20]	XIE C H , WANG J Y , ZHANG Z S ,et al. Adversarial examples for semantic segmentation and object detection[C]// Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV). Piscataway:IEEE Press, 2017: 1369-1378.
[21]	MADRY A , MAKELOV A , SCHMIDT L ,et al. Towards deep learning models resistant to adversarial attacks[C]// Proceedings of the Sixth International Conference on Learning Representations. Piscataway:IEEE Press, 2018: 1-28.
[22]	ALEXEY K , IAN G , SAMY B . Adversarial machine learning at scale[J]. arXiv Preprint,arXiv:1611.01236, 2016.
[23]	司念文, 张文林, 屈丹 ,等. 基于对抗补丁的可泛化的 Grad-CAM攻击方法[J]. 通信学报, 2021,42(3): 23-35.
	SI N W , ZHANG W L , QU D ,et al. Generalized Grad-CAM attacking method based on adversarial patch[J]. Journal on Communications, 2021,42(3): 23-35.
[24]	SU J W , VARGAS D V , SAKURAI K . One pixel attack for fooling deep neural networks[J]. IEEE Transactions on Evolutionary Computation, 2019,23(5): 828-841.
[25]	ZHONG Y Y , DENG W H . Towards transferable adversarial attack against deep face recognition[J]. IEEE Transactions on Information Forensics and Security, 2021,16: 1452-1466.
[26]	CHEN X S , YAN X Y , ZHENG F ,et al. One-shot adversarial attacks on visual tracking with dual attention[C]// Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2020: 10176-10185.
[27]	JIA S , MA C , SONG Y B ,et al. Robust tracking against adversarial attacks[C]// Proceedings of the European Conference on Computer Vision. Berlin:Springer, 2020: 69-84.
[28]	XIAO C W , LI B , ZHU J Y ,et al. Generating adversarial examples with adversarial networks[C]// Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence.[S.l.:s.n.], 2018: 835-857.
[29]	WEI X X , LIANG S Y , CHEN N ,et al. Transferable adversarial attacks for image and video object detection[C]// Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence.[S.l.:s.n.], 2019: 1-8.
[30]	JANDIAL S , MANGLA P , VARSHNEY S ,et al. AdvGAN++:harnessing latent layers for adversary generation[C]// Proceedings of 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). Piscataway:IEEE Press, 2019: 2045-2048.
[31]	DEB D , ZHANG J B , JAIN A K . AdvFaces:adversarial face synthesis[C]// Proceedings of 2020 IEEE International Joint Conference on Biometrics (IJCB). Piscataway:IEEE Press, 2020: 1-10.
[32]	BALUJA S , FISCHER I . Adversarial transformation networks:learning to generate adversarial examples[J]. arXiv Preprint,arXiv:1703.09387, 2017.
[33]	YAN B , WANG D , LU H C ,et al. Cooling-shrinking attack:blinding the tracker with imperceptible noises[C]// Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2020: 990-999.
[34]	SHARIF M , BHAGAVATULA S , BAUER L ,et al. A general framework for adversarial examples with objectives[J]. ACM Transactions on Privacy and Security, 2019,22(3): 1-30.
[35]	CARLINI N , WAGNER D . Towards evaluating the robustness of neural networks[C]// Proceedings of 2017 IEEE Symposium on Security and Privacy (SP). Piscataway:IEEE Press, 2017: 39-57.
[36]	MOOSAVI-DEZFOOLI S M , FAWZI A , FAWZI O ,et al. Universal adversarial perturbations[C]// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2017: 1765-1773.
[37]	DIN S U , AKHTAR N , YOUNIS S ,et al. Steganographic universal adversarial perturbations[J]. Pattern Recognition Letters, 2020,135: 146-152.
[38]	WU Y , LIM J , YANG M H . Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015,37(9): 1834-1848.
[39]	FAN H , LIN L T , YANG F ,et al. LaSOT:a high-quality benchmark for large-scale single object tracking[C]// Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2019: 5374-5383.
[40]	LI P X , CHEN B Y , OUYANG W L ,et al. GradNet:gradient-guided network for visual object tracking[C]// Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway:IEEE Press, 2019: 6162-6171.
[41]	GUO Q , XIE X F , JUEFEI-XU F ,et al. SPARK:spatial-aware online incremental attack against visual tracking[C]// Proceedings of the Conference on European Conference on Computer Vision. Berlin:Springer, 2020: 202-219.
[42]	LIANG S Y , WEI X X , YAO S Y ,et al. Efficient adversarial attacks for visual object tracking[C]// Proceedings of the Conference on European Conference on Computer Vision. Berlin:Springer, 2020: 34-50.

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数据集	Metric	Original	Attack S	Drop
OTB100	S(↑)	0.696	0.160	0.536
	P(↑)	0.914	0.221	0.693
VOT2018	A(↑)	0.600	0.425	0.175
	R(↓)	0.234	2.992	2.758
	EAO(↑)	0.414	0.052	0.362
LaSOT	P(↑)	0.569	0.062	0.507
	S(↑)	0.496	0.061	0.435

数据集	Metric	Original	Attack SZ	Drop
OTB100	S(↑)	0.696	0.155	0.541
	P(↑)	0.914	0.218	0.696
	A(↑)	0.600	0.395	0.205
VOT2018	R(↓)	0.234	3.030	2.796
	EAO(↓)	0.414	0.047	0.367
LaSOT	S(↓)	0.569	0.055	0.514
	Norm P(↑)	0.496	0.056	0.440

攻击方法	S(↑)	P(↑)
CSA	0.324	0.471
SPARK	0.630	0.887
FAN	0.306	0.544
本文方法	0.155	0.218

L_coarse	L _scale	L _drift	L _feature	OTB100		VOT2018
L_coarse	L _scale	L _drift	L _feature	S(↑)	P(↑)	EAO(↑)
-	-	-	-	0.696	0.914	0.414
√	-	-	-	0.421	0.587	0.099
-	√	-	-	0.443	0.642	0.149
-	-	√	-	0.587	0.791	0.159
-	-	-	√	0.470	0.655	0.142
√	√	-	-	0.349	0.491	0.073
√	√	√	-	0.302	0.423	0.061
√	√	√	√	0.160	0.221	0.047

L_coarse	L _scale	L _drift	L_feature	OTB100		VOT2018
L_coarse	L _scale	L _drift	L_feature	S(↑)	P(↑)	EAO(↑)
-	-	-	-	0.696	0.914	0.414
√	-	-	-	0.406	0.562	0.081
-	√	-	-	0.432	0.621	0.130
-	-	√	-	0.566	0.774	0.161
-	-	-	√	0.436	0.598	0.122
√	√	-	-	0.325	0.472	0.073
√	√	√	-	0.259	0.378	0.056
√	√	√	√	0.155	0.218	0.047

Multi-level loss object tracking adversarial attack method based on spatial perception

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Figures/Tables 14

References 42

Related Articles 15

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L_coarse	L_scale	L_drift	L_feature	OTB100
L_coarse	L_scale	L_drift	L_feature	S(↑)	P(↑)
-	-	-	-	0.651	0.855
√	-	-	-	0.314	0.463
-	√	-	-	0.346	0.544
-	-	√	-	0.591	0.796
-	-	-	√	0.306	0.439
√	√	-	-	0.263	0.406
√	√	√	-	0.134	0.168
√	√	√	√	0.098	0.154

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