Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 178-183.doi: 10.11896/jsjkx.210500039

• Intelligent Computing • Previous Articles     Next Articles

Projected Gradient Descent Algorithm with Momentum

WU Zi-bin, YAN Qiao   

  1. College of Computer Science & Software Engineering,Shenzhen University,Shenzhen,Guangdong 518060,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:WU Zi-bin,born in 1998.His main research interests include machine lear-ning and so on.
    YAN Qiao,born in 1972,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include network security,software-defined networking and machine learning.
  • Supported by:
    National Natural Science Foundation of China(61976142).

Abstract: In recent years,deep learning is widely used in the field of computer vision and has achieved outstanding success.However,the researchers found that the neural network is easily disturbed by adding subtle perturbations in the dataset,that can cause the model to give incorrect outputs.Such input examples are called “adversarial examples”.At present,a series of algorithms for generating adversarial examples have emerged.Based on the existing adversarial sample generation algorithm-projected gradient descent(PGD),this paper proposes an improved method-MPGDCW algorithm,which combines momentum and adopts a new loss function to ensure the stability of the update direction and avoid bad local maximums.At the same time,it can avoid the disappearance of the gradient by replacing the cross-entropy loss function.Experiments on 4 robust models containing 3 architecturesconfirm that the proposed MPGDCW algorithm has better attack effect and stronger transfer attack capacity.

Key words: Adversarial attacks, Convolutional neural network, Deep learning, Image adversarial examples

CLC Number: 

  • TP391.41
[1] SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:1-9.
[2] MIKOLOV T,KARAFIÁT M,BURGET L,et al.Recurrentneural network based language model[C]//Eleventh Annual Conference of the International Speech Communication Association.2010.
[3] HINTON G,DENG L,YU D,et al.Deep neural networks foracoustic modeling in speech recognition:The shared views of four research groups[J].IEEE Signal Processing Magazine,2012,29(6):82-97.
[4] DEVLIN J,CHANG M W,LEE K,et al.Bert:Pre-training ofdeep bidirectional transformers for language understanding[J].arXiv:1810.04805,2018.
[5] KRIZHEVSKY A,SUTSKEVER I,HINTONG E.Imagenetclassification with deep convolutional neural networks[J].Advances in Neural Information Processing Systems,2012,25:1097-1105.
[6] REN S,HE K,GIRSHICK R,et al.Faster r-cnn:Towards real-time object detection with region proposal networks[J].arXiv:1506.01497,2015.
[7] LoNG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:3431-3440.
[8] SZEGEDY C,ZAREMBA W,SUTSKEVERI,et al.Intriguing properties of neural networks[J].arXiv:1312.6199,2013.
[9] GOODFELLOW I J,SHLENS J,SZEGEDY C.Explaining and harnessing adversarial examples[J].arXiv:1412.6572,2014.
[10] KURAKIN A,GOODFELLOW I,BENGIO S.Adversarial examples in the physical world[J].arXiv:1607.02533,2016.
[11] MADRY A,MAKELOV A,SCHMIDT L,et al.Towards deep learning models resistant to adversarial attacks[J].arXiv:1706.06083,2017.
[12] CARLINI N,WAGNER D.Towards evaluating the robustness of neural networks[C]//2017 IEEE Symposium on Security and Privacy(sp).IEEE,2017:39-57.
[13] NIELSEN M A.Neural networks and deep learning(Vol.25)[M].San Francisco,CA:Determination Press,2015.
[14] POLYAK B T.Some methods of speeding up the convergence of iteration methods[J].Ussr Computational Mathematics and Mathematical Physics,1964,4(5):1-17.
[15] RUDER S.An overview of gradient descent optimization algorithms[J].arXiv:1609.04747,2016.
[16] DONG Y,LIAO F,PANG T,et al.Boosting adversarial attacks with momentum[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:9185-9193.
[17] CROCE F,HEIN M.Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks[C]//International Conference on Machine Learning.PMLR,2020:2206-2216.
[18] CROCE F,ANDRIUSHCHENKO M,SEHWAG V,et al.RobustBench:a standardized adversa-rial robustness benchmark[J].arXiv:2010.09670,2020.
[19] CROCE F,ANDRIUSHCHENKO M,SEHWAG V,et al.RobustBench/robustbench:RobustBench:a standardized adversa-rial robustness benchmark [EB/OL].
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