Exploring the Role of Artificial Intelligence in Image Forgery Detection and Prevention

A Focus on MD5 and Open CV

DOI

10.2991/978-94-6463-716-8_19How to use a DOI?

Keywords

CNN; RNN; ViTs; LSM; Hash; ELA; ResNet-50; Deepfake; GRU; GAN

Abstract

The problem of ensuring the authenticity of visual content is becoming much more pressing in such a rapid proliferation of digital media, when image forgery techniques become ever more sophisticated, more reliable methods for achieving this are required. This paper discusses a holistic approach to detecting image forgery by combining cryptographic methods with a new set of artificial intelligence (AI) methods. Several limitations of traditional detection methods such as error level analysis (ELA), which depends on the invariance of spatially local distributions within individual blocks, are examined concerning the detection of complex manipulations. We rely on cryptographic approaches to achieve high integrity verification by identifying alterations through MD5 hashing of unique hash comparisons. Further, the study employs open-source contributions of advanced image analysis such as texture, color profiling, and shape recognition to discover inconspicuous irregularities in such tampered images with OpenCV. Other AI driven models including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Vision Transformers (ViTs) further contribute to the achievement of forgery detection by leveraging multi scale feature learning, temporal analysis and self-attention. The proposed method combines MD5 hashing with these advanced AI techniques to achieve a dual layered approach for enhancing detection accuracy and adaptability to various manipulation methods including deepfake, splice, and copy move type forgeries. The proposed system is demonstrated experimentally, with significant improvements in detection accuracy and robustness over traditional methods shown. Providing a scalable and adaptable framework for preserving the integrity of digital visual content in an environment with an evolving landscape of digital manipulation, this research provides a rich set of insights about cryptographic and AI techniques integration.

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  - Mohammad Shahnawaz Shaikh
AU  - Praveen Kumar Patidar
AU  - Hemlata Patel
AU  - Mukesh Kumar
AU  - Syed Ibad Ali
PY  - 2025
DA  - 2025/05/26
TI  - Exploring the Role of Artificial Intelligence in Image Forgery Detection and Prevention
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 222
EP  - 235
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_19
DO  - 10.2991/978-94-6463-716-8_19
ID  - Shaikh2025
ER  -