Model Inversion Attack: Analysis under Gray-box Scenario on Deep Learning based Face Recognition System |
Khosravy, Mahdi
(Media Integrated Communication Laboratory, Graduate School of Engineering, Osaka University)
Nakamura, Kazuaki (Media Integrated Communication Laboratory, Graduate School of Engineering, Osaka University) Hirose, Yuki (Media Integrated Communication Laboratory, Graduate School of Engineering, Osaka University) Nitta, Naoko (Media Integrated Communication Laboratory, Graduate School of Engineering, Osaka University) Babaguchi, Noboru (Media Integrated Communication Laboratory, Graduate School of Engineering, Osaka University) |
1 | U. Aivodji, S. Gambs, and T. Ther, "GAMIN: An Adversarial Approach to Black-Box Model Inversion," arXiv preprint arXiv:1909.11835, 2019. |
2 | A. Kerckhoffs, "La cryptographic militaire," Journal des Sciences Militaries, pp. 5-38, 1883. |
3 | S. Hidano, T. Murakami, S. Katsumata, S. Kiyomoto, and G. Hanaoka, "Model inversion attacks for prediction systems: Without knowledge of non-sensitive attributes," in Proc. of the 15th Annual Conference on Privacy, Security and Trust (PST), pp. 115-11509, 2017. |
4 | M. Fredrikson, S. Jha, and T. Ristenpart, "Model inversion attacks that exploit confidence information and basic countermeasures," in Proc. of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1322-1333, 2015. |
5 | M. Khosravy, N. Gupta, N. Marina, I. K. Sethi, and M. R. Asharif, "Morphological filters: An inspiration from natural geometrical erosion and dilation," Nature-inspired Computing and Optimization, pp. 349-379, 2017. |
6 | M. Khosravy, K. Nakamura, N. Nitta, and N. Babaguchi, "Deep Face Recognizer Privacy Attack: Model Inversion Initialization by a Deep Generative Adversarial Data Space Discriminator," in Proc. of 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2020. |
7 | M. H. Sedaaghi, R. Daj, and M. Khosravi, "Mediated morphological filters," in Proc. of 2001 International Conference on Image Processing, vol. 3, pp. 692-695, 2001. |
8 | S. Hidano, T. Murakami, S. Katsumata, S. Kiyomoto, and G. Hanaoka, "Model inversion attacks for online prediction systems: Without knowledge of non-sensitive attributes," IEICE Transactions on Information and Systems, vol. 101, no. 11, pp. 2665-2676, 2018. |
9 | Y. Zhang, R. Jia, H. Pei, W. Wang, B. Li, and D. Song, "The secret revealer: generative model-inversion attacks against deep neural networks," in Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 250-258, 2020. |
10 | Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, "Vggface2: A dataset for recognizing faces across pose and age," in Proc. of the 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 67-74, 2018. |
11 | M. Khosravy, N. Gupta, N. Marina, I. K. Sethi, and M. R. Asharif, "Perceptual adaptation of image based on Chevreul-Mach bands visual phenomenon," IEEE Signal Processing Letters, vol. 24, no. 5, pp. 594-598, 2017. DOI |
12 | M. Khosravy, N. Gupta, N. Patel, and T. Senjyu, "Frontier Applications of Nature Inspired Computation," Springer, 2020. |
13 | M. Fredrikson, E. Lantz, S. Jha, S. Lin, D. Page, and T. Ristenpart, "Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing," in Proc. of the 23rd USENIX Security Symposium, vol. 14, pp. 17-32, 2014. |
14 | G. R. Shinde, A. B. Kalamkar, P. N. Mahalle, N. Dey, J. Chaki, and A. E. Hassanien, "Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art," SN Computer Science, vol. 1, no. 4, pp. 1-15, 2020. DOI |
15 | G. E. Hinton, S. Osindero, and Y. W. Teh, "A fast learning algorithm for deep belief nets," Neural Computation, vol. 18, no. 7, pp.1527-1554, 2006. DOI |
16 | L. Deng and D. Yu, "Deep learning: methods and applications," Foundations and Trends in Signal Processing, vol. 7, no. 3-4, pp. 197-387, 2004. DOI |
17 | A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems, vol. 60, no. 6, 2012. |
18 | S. Ahuja, B. K. Panigrahi, N. Dey, V. Rajinikanth, and T. K. Gandhi, "Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices," 2020. |
19 | L. Huang, A. D. Joseph, B. Nelson, B. Rubinstein, and J. D. Tygar, "Adversarial machine learning," in Proc. of the 4th ACM Workshop on Security and Artificial Intelligence, pp. 43-58, 2011. |
20 | F. Tramer, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart, "Stealing machine learning models via prediction apis," in Proc. of the 25th USENIX Security Symposium, vol. 16, pp. 601-618, 2016. |
21 | N. Papernot, P. McDaniel, A. Sinha, and M. Wellman, "Towards the science of security and privacy in machine learning," arXiv preprint arXiv:1611.03814, 2016. |
22 | G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700-4708, 2017. |
23 | N. Gupta, M. Khosravy, N. Patel, N. Dey, and O. P. Mahela, "Mendelian evolutionary theory optimization algorithm," Soft Computing, vol. 24, pp. 14345-14390, 2020. DOI |
24 | N. Gupta, N. Patel, B. N. Tiwari, and M. Khosravy, "Genetic algorithm based on enhanced selection and log-scaled mutation technique," in Proc. of the Future Technologies Conference, vol. 880, pp. 730-748, 2018. |
25 | N. Bouchra, A. Aouatif, N. Mohammed, and H. Nabil, "Deep belief network and auto-encoder for face classification," International Journal of Interactive Multimedia & Artificial Intelligence, vol. 5, pp. 22-29, 2019. DOI |
26 | N. Y. Ali, G. Sarowar, L. Rahman, J. Chaki, N. Dey, and J. Tavares, "Adam Deep Learning with SOM for Human Sentiment Classification," International Journal of Ambient Computing and Intelligence (IJACI), vol. 10, no. 3, pp. 92-116, July 2019. DOI |
27 | K. K. Verma, B. M. Singh, H. L. Mandoria, and P. Chauhan, "Two-Stage Human Activity Recognition Using 2D-ConvNet," International Journal of Interactive Multimedia & Artificial Intelligence, vol. 6, no. 2, pp. 125-135, 2020. |
28 | R. Ahuja, D. Jain, D. Sachdeva, A. Garg, and C. Rajput, "Convolutional Neural Network Based American Sign Language Static Hand Gesture Recognition," International Journal of Ambient Computing and Intelligence (IJACI), vol. 10, no. 3, pp. 60-73, 2016. DOI |
29 | G. Singh, N. Gupta, and M. Khosravy, "New crossover operators for real coded genetic algorithm (RCGA)," in Proc. of 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), pp. 135-140, 2015. |
30 | N. Gupta, M. Khosravy, O. P. Mahela, and N. Patel, "Plant biology-inspired genetic algorithm: superior efficiency to firefly optimizer," Applications of Firefly Algorithm and its Variants, pp. 193-219, 2020. |
31 | X. Wu, M. Fredrikson, S. Jha, and J. F. Naughton, "A methodology for formalizing model-inversion attacks," in Proc. of the 29th Computer Security Foundations Symposium (CSF), pp. 355-370, 2016. |
32 | D. Wang, Z. Li, N. Dey, A. S. Ashour, L. Moraru, R. S. Sherratt, and F. Shi, "Deep-segmentation of plantar pressure images incorporating fully convolutional neural networks," Biocybernetics and Biomedical Engineering, vol. 40, no. 1, pp. 546-558, 2020. DOI |
33 | A. H. Ali, A. Atia, and M. S. M. Mostafa, "Recognizing driving behavior and road anomaly using smartphone sensors," International Journal of Ambient Computing and Intelligence (IJACI), vol. 8, no. 3, pp. 22-37, 2017. DOI |
34 | F. A. Saiz, and I. Barandiaran, "COVID-19 Detection in Chest X-ray Images using a Deep Learning Approach," International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, no. 2, pp. 11-14, 2020. |
35 | N. Dey, S. Fong, W. Song, and K. Cho, "Forecasting energy consumption from smart home sensor network by deep learning," in Proc. of International Conference on Smart Trends for Information Technology and Computer Communications, pp. 255-265, 2017. |
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