Analysis of the attack effect of adversarial attacks on machine learning models

Research Article
Open access

Analysis of the attack effect of adversarial attacks on machine learning models

Guanpeng Su 1*
  • 1 University of Macau    
  • *corresponding author mc15536@um.edu.mo
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230607
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

The use of neural networks has produced outstanding results in a variety of domains, including computer vision and text mining. Numerous investigations in recent years have shown that using adversarial attacks technology to perturb the input samples weakly can mislead most mainstream neural network models, for example Fully Connected Neural Networks (FCNN) and Convolutional Neural Networks (CNN), to make wrong judgment results. Adversarial attacks can help researchers discover the potential defects of neural network models in terms of robustness and security so that people can comprehend the neural network models' learning process better and solve the neural network models' interpretability. However, suppose an adversarial attack is performed on a non-deep learning model. In that case, the results are very different from the deep learning model. This paper first briefly outlines the existing adversarial example technology; then selects the CIFAR10 dataset as the test data and LeNet, ResNet18, and VGG16 as the test model according to the technical principle; then uses the Fast Gradient Sign Attack (FGSM) method to conduct attack experiments with the CNNs and traditional machine learning algorithms like K-Nearest Neighbors (KNN) and Support Vector Machine (SVM); then analyze the experimental results and find that the adversarial example technology is specific to the deep learning model, but it cannot be completely denied that adversarial examples have no attack effect on traditional machine learning models.

Keywords:

neural network, adversarial attack, adversarial example, deep learning.

Su,G. (2023). Analysis of the attack effect of adversarial attacks on machine learning models. Applied and Computational Engineering,6,1204-1210.
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References

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[4]. H. Xu, M. Dong, D. Zhu, A. Kotov, A. I. Carcone, and S. NaarKing, “Text classification with topic-based word embedding and convolutional neural networks,” in BCB ’16 Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 2016, pp. 88–97.

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[6]. A. Kurakin, I. Goodfellow, and S. Bengio, Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533, 2016b.

[7]. Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, and Rabinovich, Andrew. Going deeper with convolutions. Technical report, arXiv preprint arXiv:1409.4842, 2014a.

[8]. I. Goodfellow, J. Shlens, and C. Szegedy,Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014b.

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[10]. Liu, D. C. and Nocedal, J. On the limited memory bfgs method for large scale optimization. Mathematical programming, 45 (1-3):503–528, 1989.

[11]. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4):541-551, Winter 1989.

[12]. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Handwritten digit recognition with a back-propagation network. In David Touretzky, editor, Advances in Neural Information Processing Systems 2 (NIPS*89), Denver, CO, 1990. Morgan Kaufman.

[13]. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.

[14]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

[15]. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

[16]. Qiu, X. Y., Kang, K., & Zhang, H. X. (2008, June). Selection of kernel parameters for KNN. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) (pp. 61-65). IEEE.

Cite this article

Su,G. (2023). Analysis of the attack effect of adversarial attacks on machine learning models. Applied and Computational Engineering,6,1204-1210.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in NIPS’12 Proceedings of the 25th International Conference on Neural Information Processing Systems, vol. 1, 2012, pp. 1097–1105.

[2]. S. Ren, K.He, R.Girshick, and J.Sun, “Fasterr-cnn: Towards real time object detection with region proposal networks,” in NIPS’15 Proceedings of the 28th International Conference on Neural Information Processing Systems, vol. 1, 2015, pp. 91–99.

[3]. I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in NIPS’14 Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014, p. 31043112.

[4]. H. Xu, M. Dong, D. Zhu, A. Kotov, A. I. Carcone, and S. NaarKing, “Text classification with topic-based word embedding and convolutional neural networks,” in BCB ’16 Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 2016, pp. 88–97.

[5]. C. Szegedy et al. ‘‘Intriguing properties of neural networks.’’ arXiv preprint arXiv: 1312.6199, 2013.

[6]. A. Kurakin, I. Goodfellow, and S. Bengio, Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533, 2016b.

[7]. Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, and Rabinovich, Andrew. Going deeper with convolutions. Technical report, arXiv preprint arXiv:1409.4842, 2014a.

[8]. I. Goodfellow, J. Shlens, and C. Szegedy,Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014b.

[9]. C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, , and R. Fergus, Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199, 2013.

[10]. Liu, D. C. and Nocedal, J. On the limited memory bfgs method for large scale optimization. Mathematical programming, 45 (1-3):503–528, 1989.

[11]. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4):541-551, Winter 1989.

[12]. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Handwritten digit recognition with a back-propagation network. In David Touretzky, editor, Advances in Neural Information Processing Systems 2 (NIPS*89), Denver, CO, 1990. Morgan Kaufman.

[13]. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.

[14]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

[15]. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

[16]. Qiu, X. Y., Kang, K., & Zhang, H. X. (2008, June). Selection of kernel parameters for KNN. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) (pp. 61-65). IEEE.

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