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Related Experiment Videos

A CPU-GPU heterogeneous parallel encryption scheme for raster remote sensing images using hybrid DNA operations and

Yi Huang1,2, Jianguo Dai3, Guoshun Zhang1

  • 1College of Information Science and Technology (College of Cyberspace Security), Shihezi University, Shihezi, China.

Scientific Reports
|July 2, 2026
PubMed
Summary

Related Concept Videos

Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

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This study introduces a novel Heterogeneous CPU-GPU Parallel Image Encryption Scheme (HC-PIES) for securing large remote sensing images. HC-PIES offers enhanced security and computational efficiency, crucial for real-time processing of geospatial data.

Area of Science:

  • Computer Science
  • Cryptography
  • Image Processing

Background:

  • High-resolution remote sensing images require robust security due to their use in meteorology, geology, and national security.
  • Conventional encryption methods struggle with large geospatial data due to memory and computational limitations.

Purpose of the Study:

  • To propose a novel Heterogeneous CPU-GPU Parallel Image Encryption Scheme (HC-PIES) addressing the security and efficiency challenges in remote sensing image encryption.
  • To leverage parallel processing capabilities of GPUs to accelerate encryption for large-scale geospatial data.

Main Methods:

  • Integration of chaotic permutations (2D-SLMM chaotic map), DNA-level operations, and cellular automaton (CA)-based diffusion.
  • A hybrid CPU-GPU architecture where the CPU handles initial global permutation and the GPU performs parallel chaotic sequence generation and DNA-CA diffusion using fused kernels.
Keywords:
CPU-GPU heterogeneous computingCellular automataChaotic systemDNA computingParallel image encryptionRemote sensing image security

Related Experiment Videos

  • Block-based GPU parallelization strategy utilizing on-chip shared memory to reduce latency and improve efficiency.

    • Main Results:

    • Achieved high security with ciphertext information entropy close to the theoretical ideal (7.9999 bits) for a [Formula: see text] image.
    • Demonstrated practical efficiency, encrypting a [Formula: see text] image in 0.0853 seconds on an entry-level GPU, showing significant acceleration over CPU implementation.
    • NPCR and UACI values met expected standards for 8-bit image encryption, confirming robust security.

    Conclusions:

    • HC-PIES provides a secure and computationally efficient solution for encrypting massive remote sensing data.
    • The heterogeneous CPU-GPU parallel approach is highly effective in overcoming the limitations of conventional encryption methods for large-scale geospatial imagery.
    • The scheme shows significant promise for real-time secure processing applications in fields utilizing remote sensing data.