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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

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

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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 intelligence Chaotic logistic map Image encryption Internet of things Machine learning Support vector machine

Related Experiment Videos

Last Updated: Jun 1, 2026

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

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.