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With the continued integration of technology in medicine, large amounts of patient data are often vulnerable to cyber-attacks. Medical data must be secured, however traditional cryptographic algorithms are inapplicable to medical images due to factors such as bulk data capacity, strong correlation among adjacent pixels, and high redundancy. To address the need for new medical image encryption algorithms, a novel approach based on the central dogma of molecular biology is proposed. The resulting algorithm has a linear runtime complexity, and is resistant to brute force, differential and statistical attacks. The algorithm advances the state-of-the-art in DNA-based image encryption and surpasses recent approaches in medical image encryption in its defence against cyber-attacks. Clinical Relevance- Secure data transmission and storage is critical for patient privacy. This algorithm increases the security of patient imaging when compared to image encryption algorithms in literature.
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http://dx.doi.org/10.1109/EMBC48229.2022.9871499 | DOI Listing |
Sci Prog
September 2025
School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China.
At present, significant progress has been made in the research of image encryption, but there are still some issues that need to be explored in key space, password generation and security verification, encryption schemes, and other aspects. Aiming at this, a digital image encryption algorithm was developed in this paper. This algorithm integrates six-dimensional cellular neural network with generalized chaos to generate pseudo-random numbers to generate the plaintext-related ciphers.
View Article and Find Full Text PDFMicrosyst Nanoeng
September 2025
Center for Terahertz Waves, College of Precision Instrument and Optoelectronics Engineering, and the Key Laboratory of Optoelectronics Information and Technology (Ministry of Education), Tianjin University, Tianjin, 300072, China.
Terahertz communication systems demand versatile devices capable of simultaneously controlling propagating waves and surface plasmon polaritons (SPPs) in far-field (FF) and near-field (NF) channels, yet existing solutions are constrained by volatile operation, single-function limitations, and the inability to integrate NF and FF functionalities. Here, we present a nonvolatile reconfigurable terahertz metasurface platform leveraging the phase-change material GeSbTe(GST) to achieve on-demand dual-channel modulation-a first in the terahertz regime. By exploiting the stark conductivity contrast of GST between amorphous and crystalline states, our design enables energy-efficient switching between NF-SPP manipulation and FF-wavefront engineering without requiring continuous power input.
View Article and Find Full Text PDFACS Appl Mater Interfaces
September 2025
National Engineering Research Center of Clean Technology in Leather Industry, Sichuan University, Chengdu 610065, PR China.
Ensuring the fidelity and security level of stored information is essential for information carrier materials to safeguard data and prevent counterfeiting. However, low resolution, limited encryption modes, and complex fabrication hinder existing information carriers from meeting evolving technological demands. Herein, a solvent exchange strategy from DMSO to water is employed to stably anchor hydrophobic fluorescent carbon dots (CDs) with multiple emission states onto a 3D framework of poly(vinyl alcohol) (PVA) chains, forming a simple two-component CDs/PVA hydrogel with tunable fluorescent colors and recyclability.
View Article and Find Full Text PDFSci Rep
September 2025
School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW, 2795, Australia.
The increasing frequency of ransomware attacks necessitates the development of more effective detection methods. Existing image-based ransomware detection approaches have largely focused on static analysis, overlooking specialized ransomware behaviors such as encryption, privilege escalation, and system recovery disruption. Although dynamic and memory forensics-based visualization methods exist in the broader malware domain, they primarily target generic malware families and often rely on memory dumps or system snapshots without transforming behavioral features into spatially meaningful representations.
View Article and Find Full Text PDFArtif Intell Med
November 2025
Department of Nuclear Medicine, Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, 313001, China. Electronic address:
Positron Emission Tomography-Computed Tomography (PET-CT) evolution is critical for liver lesion diagnosis. However, data scarcity, privacy concerns, and cross-institutional imaging heterogeneity impede accurate deep learning model deployment. We propose a Federated Transfer Learning (FTL) framework that integrates federated learning's privacy-preserving collaboration with transfer learning's pre-trained model adaptation, enhancing liver lesion segmentation in PET-CT imaging.
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