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Application of error level analysis in image spam classification using deep learning model.

Angom Buboo Singh1, Khumanthem Manglem Singh1

  • 1National Institute of Technology, Manipur, India.

Plos One
|December 14, 2023
PubMed
Summary

This study enhances image spam classification using convolutional neural networks (CNNs) and error level analysis (ELA). ELA pre-processing significantly boosts CNN accuracy, even against challenging image spam datasets.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Image spam poses a challenge for traditional classification methods due to manual feature engineering.
  • Existing methods lack robustness against adversarial attacks.
  • Convolutional Neural Networks (CNNs) offer automated feature extraction for improved classification accuracy.

Purpose of the Study:

  • To propose and evaluate a novel method for enhancing CNN performance in image spam classification.
  • To investigate the efficacy of Error Level Analysis (ELA) as a pre-processing technique for image spam detection.

Main Methods:

  • Implemented a classification pipeline utilizing CNN models for automated feature extraction.
  • Integrated Error Level Analysis (ELA) as a pre-processing step before CNN model training.

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  • Evaluated the proposed method on challenging image spam datasets designed to evade classification.

    • Main Results:

    • The proposed method incorporating ELA pre-processing demonstrated significant improvements in classification accuracy.
    • CNN models achieved higher robustness against adversarial attacks when using ELA.
    • The approach proved effective even on difficult datasets specifically designed to challenge classification systems.

    Conclusions:

    • Error Level Analysis (ELA) is a valuable pre-processing technique for improving CNN-based image spam classification.
    • The proposed ELA-enhanced CNN method offers a more robust and accurate solution for detecting image spam.
    • This approach advances the field of spam detection and cybersecurity by leveraging advanced image analysis and deep learning techniques.