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Related Experiment Video

Updated: Jun 1, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Machine learning-driven image encryption using SVM for enhanced security and computational efficiency.

Saba Inam1, Shamsa Kanwal2, Sumaira Mushtaq2

  • 1Department of Mathematical Sciences, Fatima Jinnah Women University, The Mall, Rawalpindi, Pakistan. saba.inam@fjwu.edu.pk.

Scientific Reports
|May 30, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a novel machine learning (ML) approach for image encryption, enhancing space efficiency while maintaining high security. The selective encryption method significantly reduces computational overhead for real-time applications.

Area of Science:

  • Computer Science
  • Information Security
  • Machine Learning

Background:

  • Traditional image encryption methods face challenges in balancing security with computational efficiency, limiting real-time applications.
  • Achieving high resilience often requires significant computational resources, hindering practical deployment.

Purpose of the Study:

  • To develop a novel image encryption paradigm combining Machine Learning (ML) for improved space efficiency and security.
  • To address the computational overhead associated with traditional image encryption techniques.

Main Methods:

  • Utilized a Support Vector Machine (SVM) to classify image pixel blocks into low, moderate, and high information content.
  • Implemented a selective encryption process encoding only moderate and high information blocks, leaving low information blocks unaltered.
Keywords:
Artificial intelligenceChaotic logistic mapImage encryptionInternet of thingsMachine learningSupport vector machine

Related Experiment Videos

Last Updated: Jun 1, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Main Results:

  • Achieved exceptional security evaluation metrics: 97.4% accuracy, 7.999 entropy, 0.0001 correlation, and 0.0153 energy.
  • Demonstrated a considerable reduction in processing overhead through selective encryption.
  • Validated the technique's effectiveness in preserving security while enhancing space efficiency.

Conclusions:

  • The proposed ML-driven selective image encryption offers a unique, reproducible, and efficient solution for secure communications.
  • This research advances image encryption by elaborating on computational aspects of ML-driven selective encryption.
  • The method provides a viable approach for secure data transfer in complex modern communication systems.