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On Combining Deep Neural Network Classifiers for Source Device Identification
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AI
DOI：10.1109/ACCESS.2025.3555141.png)
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摘要
En 中文
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.
Keyword:
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
