Fake human face recognition with classical-quantum hybrid transfer learning

Year 2021, Volume: 1 Issue: 1, 46 - 55, 28.02.2021

Furkan Ciylan , Bünyamin Ciylan

Abstract

Many security applications like face recognition and iris recognition developed to ensure the safety of the critical or personal data. Reliability of these applications are highly depending over the reliability of machine vision algorithms. Along with the development of the generative models standard machine vision dependent data security measurement systems became vulnerable. Currently, generating fake data to bypass machine vision dependent security systems is possible with a personal computer. In order to ensure the reliability of security measurements depending on the machine vision techniques it is critical to recognize fake images. In this research, possible use case of a quantum computer to ensure the reliability of machine vision dependent security systems is investigated. A hybrid quantum-classical hybrid model with the transfer learning approach is proposed to recognize whether if a face is fake or not. Effects of the quantum model’s depth over the accuracy is explored. ResNet-18 architecture is used as the classical part and a custom quantum neural network architecture built with the dressed quantum circuits is used as quantum part. This research is aimed to extend the use cases of quantum neural networks to security applications. Accuracies of quantum neural networks with different depths are reported. A simulated quantum computer is used to train the models. Along with the proposed approach it is concluded that it is possible to apply classical-quantum neural networks to improve the reliability of machine vision dependent security systems after the quantum co-processors become available in daily life.

Keywords

Deep Learning, Fake Image, Image Classification, Transfer Learning, Quantum, Quantum Machine Learning

References

• [1] Voulodimos, A., Doulamis, N., Doulamis A., & Protopapadakis E. Deep Learning for Computer Vision: A Brief Review. Computational Intelligence and Neuroscience 2018, 7068349.
• [2] Fukushima, K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybernetics 1980, 36, 193–202.
• [3] Feynman, R. P. Int. J. Theor. Phys. 1982, 21, 467.
• [4] Nielsen, M. A., Chuang, I. L. Quantum Computation and Quantum Information (Cambridge University, New York, 2009).
• [5] Shor P. W. SIAM J. Comput. 1997, 26, 1484.
• [6] Preskill, J. Quantum Computing NISQ era and Beyond, 2018, arXiv:1801.00862.
• [7] Lewestein, M. Quantum Perceptrons, Journal of Modern Optics 1994, 41, (12), 2491-2501.
• [8] Chrisley, R.L. Quantum learning, in: P. Pylkka ̈nen, P. Pylkkö (Eds.), New Directions in Cognitive Science, Proceedings of the International Symposium, Saariselka ̈, Finnish Artifcial Intelligence Society, Lapland, Finland, 1995.
• [9] Chrisley, R.L. Learning in non-superpositional quantum neurocomputers, in: P. Pylkka ̈anen, P.Pylkkö (Eds.), Brain Mind and Physics, IOS Press, Amsterdam, 1997, pp. 126*139.
• [10] Menneer, T., Narayanan, A. Quantum inspired neural networks, Department of Computer Science, University of Exeter, UK, http://www.dcs.ex.ac.uk/reports/reports.html, 1995.
• [11] Menneer, T., Narayanan, A. Quantum Artificial Neural Networks vs Classical Artificial Neural Networks: Experiments in Simulation, Proceedings of the Fifth Joint Conference on Information Sciences, 2000, vol. 1, pp. 757-759.
• [12] Dallaire-Demers, P., Killoran, N. Quantum Generative Adversarial Networks, Phys. Rev. A, 2018, 98, 012324
• [13] Killoran, N., Bromley, T.R., Arrazola, J.M., Schuld, M., Quesada, N. & Lloyd, S. Continous-Variable Quantum Neural Networks, 2018, arXiv:1806.06871.
• [14] Xia, R., Kais, S. Hybrid Quantum-Classical Neural Network for Calculating Ground State Energies of Molecules, 2019, arXiv:1902.06184.
• [15] Mari, A., Bromley, T.R., Izaac, J., Schuld, M., & Killoran, N. Transfer Learning in Hybrid Classical-Quantum Neural Networks, 2019, arXiv:1912.08278.
• [16] Farhi, E., Neven, H., Classification with quantum neural networks on near term processors, 2018, arXiv:1802.06002.
• [17] Liu, D., Ran, S., Wittek, P., Peng, C., Garc ́ıa, R.C., Su, G., & Lewenstein, M. Machine learning by unitary ten-sor network of hierarchical tree structure. New Journal of Physics, 2019, 21, (7), 073059.
• [18] Perdomo-Ortiz,A., Benedetti, M., Realpe-G ́omez,J., & Biswas, R. Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers. Quantum Science and Technology, 2018, 3, (3), 030502.
• [19] Peruzzo, A., McClean, J., Shad-bolt, P., Yung, M., Zhou, X., JLove, P., Aspuru-Guzik, A., & L O’brien, J. A variational eigenvalue solver on a photonic quantum processor. Nature Communications, 2014, 5, 4213.
• [20] Schuld, M., Killoran, N. Quantum machine learning in feature Hilbert spaces. Physical Review Letters, 2019, 122, (4), 040504.
• [21] Schuld, M., Bocharov, A., Svore K. M., & Wiebe, N. Circuit-centric quantum classifiers. Physical Review A, 2020, 101, (3).
• [22] Sim, S., Johnson, P.D., & Aspuru-Guzik, A. Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies, 2019, 2(12), 1900070.
• [23] Yonsei University Computational Intelligence and Photography Lab, Real and Fake Face Detection, 2019, Retrieved 08.02.2021 from: https://www.kaggle.com/ciplab/real-and-fake-face-detection.