Image Forgery Detection - Deep Learning Approaches: Studying deep learning approaches for detecting image forgeries and manipulations, including techniques for identifying digital tampering

Authors

  • Dr. Svetlana Bozhko Professor of Applied Mathematics, Belarusian State University Author

Keywords:

Image forgery detection, deep learning

Abstract

Image forgery and manipulation have become prevalent with the rise of digital media, leading to challenges in verifying the authenticity of images. This paper presents a comprehensive study of deep learning approaches for detecting image forgeries, focusing on techniques for identifying digital tampering. We review recent advancements in deep learning models, datasets, and evaluation metrics for image forgery detection. Additionally, we discuss the limitations of existing approaches and propose future research directions to enhance the robustness of image forgery detection systems.

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References

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Published

06-05-2022

How to Cite

[1]
Dr. Svetlana Bozhko, “Image Forgery Detection - Deep Learning Approaches: Studying deep learning approaches for detecting image forgeries and manipulations, including techniques for identifying digital tampering”, Distrib Learn Broad Appl Sci Res, vol. 8, pp. 108–117, May 2022, Accessed: Nov. 07, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/61

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