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.

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