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.