Deep Learning in Medical Forensics and Neurodegeneration: A Survey on Tampered Image Detection and Alzheimer’s Diagnosis

DOI

10.2991/978-94-6239-616-6_17How to use a DOI?

Keywords

Medical image tampering; Deepfake detection; Alzheimer’s disease prediction; MRI scans; Image forensics; 3D-CNN; ResNet-18; Transfer learning; Copy-move forgery; Splicing; Pixel manipulation; GAN-based deepfakes; Medical image authentication; DenseNet; LeNet

Abstract

The widespread use of digital medical imaging modalities such as MRI, CT, and X-ray has increased the risk of malicious manipulation through deepfake techniques, including artificial atrophy insertion, pixel modification, and GAN-generated forgeries. Such tampering can mislead clinicians, compromise diagnostic accuracy, and endanger patient safety, underscoring the need for reliable medical image authentication. This study presents an advanced dual-stage framework that integrates deep learning and image forensics to both detect medical image manipulation and provide accurate Alzheimer’s disease predictions. In the first stage, tampered and untampered medical scans are analyzed using a hybrid forensic architecture that combines handcrafted feature extraction with deep models such as ResNet and CNNs, enabling precise detection of copy-move, splicing, and GAN-based deepfakes across multiple imaging modalities. The second stage employs volumetric learning and transfer-learning approaches, including 3D-CNN and ResNet-18, to classify Alzheimer’s disease into its respective stages with high accuracy. Experimental results demonstrate that the proposed system achieves superior performance in both forgery detection and disease prediction, offering a robust, trustworthy, and clinically valuable solution for modern healthcare environments.

Copyright

© 2026 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  - B. Ananth
AU  - C. Sreenand
AU  - L. V. Shyaam
AU  - M. S. Neyan
PY  - 2026
DA  - 2026/03/31
TI  - Deep Learning in Medical Forensics and Neurodegeneration: A Survey on Tampered Image Detection and Alzheimer’s Diagnosis
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 201
EP  - 211
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_17
DO  - 10.2991/978-94-6239-616-6_17
ID  - Ananth2026
ER  -