On Combining Deep Neural Network Classifiers for Source Device Identification

doi:10.1109/ACCESS.2025.3555141

On Combining Deep Neural Network Classifiers for Source Device Identification

2025-01-01

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Ioannis Tsingalis *

C

Constantine Kotropoulos

DOI：10.1109/ACCESS.2025.3555141

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This paper proposes combining deep neural network classifiers while simultaneously optimizing the networks. The proposed combination scheme enhances the accuracy of each classifier, which, in turn, boosts the overall combined accuracy during a post-processing step. The proposed classification scheme is thoroughly evaluated on a dataset specifically designed for multimedia forensics research. The combined classifiers include shallow and deep neural networks, with input data comprising original and manipulated content processed through online social networks such as YouTube, WhatsApp, and Facebook. The experimental results demonstrate promising performance, proving the usability of the proposed classifier combination scheme. Specifically, it is observed that the accuracy of shallow neural networks improves significantly when combined with deep neural networks. This performance enhancement is particularly notable when the combined classifiers are trained on data manipulated by online social network platforms.

Keywords:

Accuracy

Forensics

Object recognition

Videos

Cameras

Artificial neural networks

Social networking (online)

Smart phones

Media

Image coding

Classifier combination

fusion

product rule

ensemble learning

source device identification (SDI)

multimodal

multimedia forensics

On Combining Deep Neural Network Classifiers for Source Device Identification

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