Integrating Fractional-Order Hopfield Neural Network with Differentiated Encryption: Achieving High-Performance Privacy Protection for Medical Images
Panzhihua University · University of Electronic Science and Technology of China · +1 more institution
Abstract
Medical images demand robust privacy protection, driving research into advanced image encryption (IE) schemes. However, current IE schemes still encounter certain challenges in both security and efficiency. Fractional-order Hopfield neural networks (HNNs) demonstrate unique advantages in IE. The introduction of fractional-order calculus operators enables them to possess more complex dynamical behaviors, creating more random and unpredictable keystreams. To enhance privacy protection, this paper introduces a high-performance medical IE scheme that integrates a novel 4D fractional-order HNN with a differentiated encryption strategy (MIES-FHNN-DE). Specifically, MIES-FHNN-DE leverages this 4D fractional-order HNN…
Citation impact
- FWCI
- 57.25
- Percentile
- 100%
- References
- 52
Authors
9Topics & keywords
- Encryption
- Computer science
- Order (exchange)
- Artificial neural network
- Hopfield network
- Privacy protection
- Computer network
- Computer security