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Exploring JPEG File Containers Without Metadata : A Machine Learning Approach for Encoder Classification

This thesis explores a method for identifying JPEG encoders without relying on metadata by analyzing characteristics inherent to the JPEG file format itself. The approach uses machine learning to differentiate encoders based on features such as quantization tables, Huffman tables, and marker sequences. These features are extracted from the file container and analyzed to identify the source encoder. The random forest classification algorithm was applied to test the efficacy of the approach across different datasets, aiming to validate the model's performance and reliability. The results confirm the model's capability to identify JPEG source encoders, providing a useful approach for digital forensic investigations.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:hh-53596
Date January 2024
CreatorsIko Mattsson, Mattias, Wagner, Raya
PublisherHögskolan i Halmstad, Akademin för informationsteknologi
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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