Cryptanalysis of computational optical ghost imaging cryptosystems via deep learning

Yabin Zhang, Zhiwen Jin, Mingguang Shan +5 more

Shengmei Zhao, Le Wang, Wenqiang Liang +2 more

Silvano Donati, ANNOVAZZI-LODI Valerio ANNOVAZZI-LODI Valerio, 王昭 Wang Zhao

Chané Moodley, Bereneice Sephton, Valeria Rodríguez-Fajardo +1 more

Quantum ghost imaging utilizes entangled photon pairs to enable an alternative image acquisition method. Information from either one of the photons does not allow for image reconstruction, however the image can be reconstructed by utilising the correlations that exist between the photon pair. Interestingly, these photon pairs can be either degenerate or non-degenerate in nature. Non-degenerate ghost imaging offers the ability to image with wavelength bandwidths where spatially resolving detectors are impractical, ineffective or expensive. Due to the scanning nature of spatially resolving detectors and the inherent low light levels of quantum experiments, imaging speeds are rather unsatisfactory. To overcome this limitation, we propose a two-step deep learning approach to establish an optimal early stopping point, tested on a non-degenerate system. In step one, we enhance the reconstructed image after each measurement by a deep convolution auto-encoder, followed by step two where a classifier is used to recognise the image. We achieved a recognition confidence of 75% at 20% of the image reconstruction time, hence reducing the image reconstruction time 5-fold while maintaining the image information. This, therefore, leads to a faster, more efficient image acquisition technique. Although tested on a non-degenerate system, our procedure can be extended to many such systems that are of quantum nature. We believe that this two-step deep learning approach will prove valuable to the community who are focusing their efforts on time-efficient ghost imaging.

Behruz Ramasubbu

The rapid growth of Internet of Things (IoT)-enabled healthcare systems has led to the widespread use of cloud-based platforms for storing and transmitting medical images. While this paradigm improves accessibility and scalability, it also introduces significant security challenges, including unauthorized access, data breaches, and cyberattacks. Secure medical image cryptanalysis has therefore become a critical research area, focusing on evaluating and strengthening encryption techniques used in healthcare systems. Recent advancements in artificial intelligence, particularly deep learning and quantum neural networks (QNNs), have opened new avenues for enhancing medical image security. Quantum neural networks leverage principles of quantum computing such as superposition and entanglement to improve computational efficiency and encryption strength. Additionally, deep learning-based cryptographic techniques enable intelligent key generation, anomaly detection, and attack mitigation. This paper presents a comprehensive review of secure medical image cryptanalysis techniques using quantum neural networks in IoT-enabled cloud storage environments. The study explores hybrid approaches combining quantum-inspired algorithms, deep learning models, and cryptographic techniques to improve data confidentiality, integrity, and availability. Recent research highlights the effectiveness of quantum-enhanced encryption frameworks, such as quantum key distribution and hybrid cryptographic systems, in protecting sensitive medical data. Despite these advancements, challenges such as high computational complexity, limited quantum hardware availability, and scalability issues persist. This review analyses recent developments, identifies research gaps, and provides future directions for developing secure and efficient medical image cryptanalysis systems.

Juan Wu, Xiao-Hai Zhan, Fang‐Xiang Wang +6 more