Comparative Analysis and Review of Deep Learning Techniques for Digital Image Tampering Detection

DOI

10.2991/978-94-6463-823-3_17How to use a DOI?

Keywords

Image Tampering Detection; Convolutional Neural Network; Tampering Localization; Deep Learning

Abstract

With the rapid development of the internet and advancements in digital imaging technology, the technical barriers and costs associated with digital image processing have significantly decreased. Ordinary image editing software now enables users to readily modify specific regions of original images. However, the widespread dissemination of tampered images on the internet can severely impact individual privacy, social stability, and even national security. In today’s high-tech era, the majority of tampered images are nearly indistinguishable to the naked eye, rendering technological solutions indispensable for identifying manipulation traces. In recent years, the evolution of artificial intelligence (AI) has facilitated its integration into diverse industries, including image tampering detection. Numerous research teams have incorporated AI-driven methodologies into this field, significantly enhancing the accuracy and efficiency of detection and localization. This paper systematically reviews and summarizes state-of-the-art AI-based deep learning techniques for image tampering detection developed in recent years. It provides a comprehensive analysis of datasets and performance evaluation metrics employed in large-scale model training by various research groups, alongside technical comparisons and performance assessments of different detection models. Finally, the study critically synthesizes existing limitations and urgent challenges in current tampering detection technologies, while offering insights into future prospects for advancements in AI-integrated tampering detection methodologies. The findings of this review aim to provide insights into the current trends and future directions in the field of image tampering detection, contributing to the development of more reliable and scalable solutions for digital image forensics.

Copyright

© 2025 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  - Weixi Yin
PY  - 2025
DA  - 2025/08/31
TI  - Comparative Analysis and Review of Deep Learning Techniques for Digital Image Tampering Detection
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 186
EP  - 199
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_17
DO  - 10.2991/978-94-6463-823-3_17
ID  - Yin2025
ER  -