Quantum-Inspired Deep Learning for Secure Medical Image Encryption: A Classical Implementation with Enhanced Chaotic Complexity
DOI:
https://doi.org/10.65204/djes.v3i3.937Keywords:
quantum-inspired encryption; artificial intelligence; medical image security; chaotic systems; convolutional neural networks; classical post-quantum-inspired security; cybersecurity; IoT healthcareAbstract
The protection of medical imaging data has become an increasingly important concern as cloud-based healthcare and telemedicine proliferate. AES and RSA are two examples of traditional encryption schemes susceptible to attacks by powerful quantum computers using Shor’s algorithm. The purpose of this study is to propose a new architecture that improves encryption by combining classical representations of quantum concepts with a convolutional neural network (CNN). Entropy, edge density, and texture complexity are the three statistical descriptors generated by the CNN after analysing images. These descriptors are used to dynamically modify encryption settings. In testing approximately 5,000 medical images (MRI, CT, ultrasound, and radiography), promising results were obtained. These results included an entropy of 7.999, a reduction in adjacent-pixel correlations of 0.0001, an NPCR of 99.81%, and a UACI of 33.8%. Additionally, processing times were 18% lower than those of traditional methods, making it suitable for information and communication technology (IoT)- based healthcare settings with limited resources. It should be noted that the term “quantum-inspired” in this work refers to algorithmic principles drawn from quantum mechanics implemented entirely on classical hardware and does not imply compliance with NIST post-quantum cryptographic standards.
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