A Comprehensive Research of Image Tampering Detection Techniques Based on Deep Learning

DOI

10.2991/978-94-6463-518-8_8How to use a DOI?

Keywords

Deep Learning; Image Tampering Detection; Convolutional Neural Networks (CNNs); Networks (GANs); Digital Forensics

Abstract

This paper reviews the advancements in image tampering detection technologies driven by deep learning. Traditional methods, dependent on manually crafted features, often fall short when confronting sophisticated tampering techniques. In contrast, deep learning models such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Recurrent Neural Networks (RNNs) significantly enhance detection capabilities through robust feature extraction and pattern recognition. These models excel in various aspects of detection: CNNs in spatial analysis, GANs in improving robustness via adversarial training, and RNNs in capturing temporal sequences in data. Despite facing challenges like the necessity for extensive annotated datasets and issues with interpretability, ongoing research is dedicated to refining these models for better generalization and efficiency. Ongoing research is enhancing model efficiency and generalization, with future work focusing on integrating multimodal data and developing more interpretable deep-learning models to ensure the integrity of visual content. Future directions aim to expand upon current capabilities by integrating multimodal data and developing models that are easier to interpret, thus ensuring the integrity and authenticity of visual content across various digital platforms.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bosheng Yang
PY  - 2024
DA  - 2024/09/28
TI  - A Comprehensive Research of Image Tampering Detection Techniques Based on Deep Learning
BT  - Proceedings of the 2024 International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2024)
PB  - Atlantis Press
SP  - 66
EP  - 74
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-518-8_8
DO  - 10.2991/978-94-6463-518-8_8
ID  - Yang2024
ER  -