A Deep Learning Framework to identify real-world stego images

Lavania, Khushboo (2021) A Deep Learning Framework to identify real-world stego images. Masters thesis, Dublin, National College of Ireland.

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Abstract

Image Steganalysis is the process of identifying images that have been processed using Steganography to hide information or messages. These images are called stego images. Identifying real-world stego images which vary in dimensions, scalability, lightning conditions with unknown steganography algorithms, payload, embedding capacity is a challenging task. This research proposes a deep learning framework that uses Convolutional Neural Network (CNN) to identify such real-world stego images. The framework consists of two pre-trained models namely InceptionNet V3 and EfficientNet B3. Models are trained using the ALASKA2 dataset and are evaluated using model accuracy. Additionally, evaluation metrics like Precision, Recall, and F1 scores are also calculated. Inception V3 achieves an accuracy of 73.33% while the performance of EfficientNet B3 is slightly better with an accuracy of 79.43%. Identification of real-world stego images will be of great benefit to the government security department to determine any illegal activity.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Clara Chan
Date Deposited:	06 Dec 2021 15:45
Last Modified:	06 Dec 2021 15:45
URI:	https://norma.ncirl.ie/id/eprint/5182

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