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Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection.

N Krishnaraj1, B Sivakumar2, Ramya Kuppusamy3

  • 1Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur 603203, Chennai, India.

Computational Intelligence and Neuroscience
|February 10, 2022
PubMed
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This summary is machine-generated.

This study introduces a new deep learning fusion model (DLFM-CMDFC) for detecting copy-move image forgeries. The advanced technique effectively identifies manipulated images, outperforming existing methods.

Area of Science:

  • Computer Vision
  • Digital Image Forensics
  • Artificial Intelligence

Background:

  • The proliferation of sophisticated fake images necessitates advanced forgery detection tools.
  • Traditional methods struggle with large datasets, while deep learning models face generalization issues.
  • Copy-move forgery, where image regions are duplicated, is a common manipulation technique.

Purpose of the Study:

  • To propose an automated deep learning-based fusion model for detecting and localizing copy-move forgeries (DLFM-CMDFC).
  • To enhance the accuracy and generalization capabilities of forgery detection systems.

Main Methods:

  • A fusion model combining Generative Adversarial Networks (GANs) and Densely Connected Networks (DenseNets).
  • Integration of an Extreme Learning Machine (ELM) classifier with optimized weights and biases via the Artificial Fish Swarm Algorithm (AFSA).

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  • Utilizing a merger unit to process network outputs for forgery identification.

    • Main Results:

    • The DLFM-CMDFC model demonstrated superior performance in detecting copy-move forgeries.
    • Validation on two benchmark datasets confirmed the model's effectiveness.
    • The proposed method showed significant improvements over recently developed approaches.

    Conclusions:

    • The developed DLFM-CMDFC model offers a robust and automated solution for copy-move forgery detection.
    • The fusion of GANs, DenseNets, and ELM with AFSA optimization presents a promising direction for digital image forensics.
    • This approach addresses the limitations of traditional methods and current deep learning models.