One evolutionary algorithm deceives humans and ten convolutional neural networks trained on ImageNet at image recognition

TOPAL, Ali Osman; CHITIC, Ioana Raluca; LEPREVOST, Franck

doi:10.1016/j.asoc.2023.110397

Article (Scientific journals)

One evolutionary algorithm deceives humans and ten convolutional neural networks trained on ImageNet at image recognition

TOPAL, Ali Osman; CHITIC, Ioana Raluca; LEPREVOST, Franck

2023 • In Applied Soft Computing, 143, p. 110397

Peer Reviewed verified by ORBi

Permalink
https://hdl.handle.net/10993/55399

DOI
10.1016/j.asoc.2023.110397

Files (1)Send to Details Statistics Bibliography Similar publications

Files

Full Text

1-s2.0-S1568494623004155-main.pdf

Publisher postprint (7.38 MB)

Download

All documents in ORBilu are protected by a user license.

Send to

RIS BibTex APA Chicago Permalink X Linkedin

Details

Keywords :

Adversarial attacks; Black-box attacks; Convolutional neural network; Evolutionary algorithm; Image classification

Abstract :

[en] Convolutional neural networks (CNNs) are widely used in computer vision, but can be deceived by carefully crafted adversarial images. In this paper, we propose an evolutionary algorithm (EA) based adversarial attack against CNNs trained on ImageNet. Our EA-based attack aims to generate adversarial images that not only achieve a high confidence probability of being classified into the target category (at least 75%), but also appear indistinguishable to the human eye in a black-box setting. These constraints are implemented to simulate a realistic adversarial attack scenario. Our attack has been thoroughly evaluated on 10 CNNs in various attack scenarios, including high-confidence targeted, good-enough targeted, and untargeted. Furthermore, we have compared our attack favorably against other well-known white-box and black-box attacks. The experimental results revealed that the proposed EA-based attack is superior or on par with its competitors in terms of the success rate and the visual quality of the adversarial images produced.

Research center :

ULHPC - University of Luxembourg: High Performance Computing

Disciplines :

Computer science

Author, co-author :

TOPAL, Ali Osman ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)

CHITIC, Ioana Raluca ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)

LEPREVOST, Franck ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)

External co-authors :

Language :

English

Title :

One evolutionary algorithm deceives humans and ten convolutional neural networks trained on ImageNet at image recognition

Publication date :

11 May 2023

Journal title :

Applied Soft Computing

ISSN :

1568-4946

eISSN :

1872-9681

Publisher :

Elsevier

Volume :

143

Pages :

110397

Peer reviewed :

Peer Reviewed verified by ORBi

Focus Area :

Computational Sciences

Additional URL :

https://www.sciencedirect.com/science/article/pii/S1568494623004155

Available on ORBilu :

since 22 June 2023

Statistics

Number of views

103 (10 by Unilu)

Number of downloads

81 (3 by Unilu)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenAlex citations

Bibliography

Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H., Training data-efficient image transformers & distillation through attention. International Conference on Machine Learning, 2021, PMLR, 10347–10357.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the Inception architecture for computer vision, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2016, pp. 2818–2826, URL https://ieeexplore.ieee.org/document/7780677.
G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4700–4708.
Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition. 2014 arXiv preprint arXiv:1409.1556.
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., Gradient-based learning applied to document recognition. Proc. IEEE 86:11 (1998), 2278–2324.
Wang, Z.J., Turko, R., Shaikh, O., Park, H., Das, N., Hohman, F., Kahng, M., Chau, D.H.P., CNN explainer: Learning convolutional neural networks with interactive visualization. IEEE Trans. Vis. Comput. Graphics 27:2 (2020), 1396–1406.
S. Thys, W. Van Ranst, T. Goedemé, Fooling automated surveillance cameras: adversarial patches to attack person detection, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.
Fawzi, A., Fawzi, H., Fawzi, O., Adversarial vulnerability for any classifier. Adv. Neural Inf. Process. Syst., 31, 2018.
Chitic, R., Topal, A.O., Leprévost, F., Evolutionary algorithm-based images, humanly indistinguishable and adversarial against convolutional neural networks: efficiency and filter robustness. IEEE Access 9 (2021), 160758–160778.
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R., Intriguing properties of neural networks. 2013 arXiv preprint arXiv:1312.6199.
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A., The limitations of deep learning in adversarial settings. 2016 IEEE European Symposium on Security and Privacy (EuroS&P), 2016, IEEE, 372–387 URL https://ieeexplore.ieee.org/document/7467366.
Su, J., Vargas, D.V., Sakurai, K., One pixel attack for fooling deep neural networks. IEEE Trans. Evol. Comput. 23:5 (2019), 828–841.
Wu, J., Generating adversarial examples in the harsh conditions. 2020 CoRR abs/1908.11332. URL https://arxiv.org/abs/1908.11332.
Jere, M., Rossi, L., Hitaj, B., Ciocarlie, G., Boracchi, G., Koushanfar, F., Scratch that! An evolution-based adversarial attack against neural networks. 2019 CoRR abs/1912.02316. URL https://arxiv.org/abs/1912.02316.
Chitic, R., Leprévost, F., Bernard, N., Evolutionary algorithms deceive humans and machines at image classification: an extended proof of concept on two scenarios. J. Inf. Telecommun., 2020, 1–23.
Chitic, R., Bernard, N., Leprévost, F., A proof of concept to deceive humans and machines at image classification with evolutionary algorithms. Intelligent Information and Database Systems, 12th Asian Conference, ACIIDS 2020 (Phuket, Thailand, March 23-26, 2020), 2020, Springer, Heidelberg, 467–480.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L., The ImageNet image database. 2009 http://image-net.org.
Fezza, S.A., Bakhti, Y., Hamidouche, W., Déforges, O., Perceptual evaluation of adversarial attacks for CNN-based image classification. 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), 2019, IEEE, 1–6.
Carlini, N., Wagner, D., Towards evaluating the robustness of neural networks. 2017 IEEE Symposium on Security and Privacy (SP), 2017, IEEE, 39–57.
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E., Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 13, 2004 URL https://ieeexplore.ieee.org/document/128439.
Eiben, A.E., Smith, J.E., Introduction to Evolutionary Computing. 2003, Springer URL https://www.springer.com/gp/book/9783642072857.
Bernard, N., Leprévost, F., Evolutionary algorithms for convolutional neural network visualisation. High Performance Computing – 5th Latin American Conference, CARLA 2018 (Bucaramanga, Colombia, Sep 23-28, 2018) Communications in Computer and Information Science, vol. 979, 2018, Springer, Heidelberg, 18–32.
Doerr, B., Runtime analysis of evolutionary algorithms via symmetry arguments. Inform. Process. Lett., 166, 2021, 106064.
Forrest, S., Mitchell, M., What makes a problem hard for a genetic algorithm? Some anomalous results and their explanation. Mach. Learn. 13:2 (1993), 285–319.
Varrette, S., Bouvry, P., Cartiaux, H., Georgatos, F., Management of an academic HPC cluster: The UL experience. Proceedings of the 2014 International Conference on High Performance Computing & Simulation (HPCS 2014), 2014, IEEE, 959–967.
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H., Mobilenets: Efficient convolutional neural networks for mobile vision applications. 2017 arXiv preprint arXiv:1704.04861.
B. Zoph, V. Vasudevan, J. Shlens, Q.V. Le, Learning transferable architectures for scalable image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 8697–8710.
Chollet, F., et al. Keras. 2015 https://keras.io.
Van Rossum, G., Drake, F.L., Python 3 Reference Manual. 2009, CreateSpace, Scotts Valley, CA.
Oliphant, T.E., A Guide to NumPy. 2006, Trelgol Publishing USA.
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X., TensorFlow: Large-scale machine learning on heterogeneous systems. 2015 URL https://www.tensorflow.org/. Software available from tensorflow.org.
Van der Walt, S., Schönberger, J.L., Nunez-Iglesias, J., Boulogne, F., Warner, J.D., Yager, N., Gouillart, E., Yu, T., the scikit-image contributors, J., Scikit-image: image processing in python. PeerJ, 2, 2014, e453, 10.7717/peerj.453.
Goodfellow, I.J., Shlens, J., Szegedy, C., Explaining and harnessing adversarial examples. 2015 CoRR abs/1810.00069. arXiv:1412.6572. URL http://arxiv.org/abs/1412.6572.
Kurakin, A., Goodfellow, I.J., Bengio, S., Adversarial examples in the physical world. 2016 CoRR abs/1607.02533. arXiv:1607.02533. URL http://arxiv.org/abs/1607.02533.
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A., Towards deep learning models resistant to adversarial attacks. 2019 CoRR arXiv:1706.06083. URL http://arxiv.org/abs/1706.06083.
Guo, C., Gardner, J., You, Y., Wilson, A.G., Weinberger, K., Simple black-box adversarial attacks. International Conference on Machine Learning, 2019, PMLR, 2484–2493.
Xiao, C., Li, B., Zhu, J.-Y., He, W., Liu, M., Song, D., Generating adversarial examples with adversarial networks. 2018 arXiv preprint arXiv:1801.02610.
Nicolae, M., Sinn, M., Minh, T.N., Rawat, A., Wistuba, M., Zantedeschi, V., Molloy, I.M., Edwards, B., Adversarial robustness toolbox v1.0.0. 2018 CoRR abs/1807.01069. URL http://arxiv.org/abs/1807.01069.
Targonski, C., Tensorflow implementation of generating adversarial examples with adversarial networks. 2019 URL https://github.com/ctargon/AdvGAN-tf/.
Jain, N., Keras implementation of AdvGAN. 2019 URL https://github.com/niharikajainn/adv_gan_keras.