On Combining Deep Neural Network Classifiers for Source Device Identification
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...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10942326/ |
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| author | Ioannis Tsingalis Constantine Kotropoulos |
| author_facet | Ioannis Tsingalis Constantine Kotropoulos |
| author_sort | Ioannis Tsingalis |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-23a2976f29814b3185b2013b2d303e92 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-23a2976f29814b3185b2013b2d303e922025-08-20T01:54:41ZengIEEEIEEE Access2169-35362025-01-0113584255844110.1109/ACCESS.2025.355514110942326On Combining Deep Neural Network Classifiers for Source Device IdentificationIoannis Tsingalis0https://orcid.org/0000-0003-2590-325XConstantine Kotropoulos1https://orcid.org/0000-0001-9939-7930Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, GreeceDepartment of Informatics, Aristotle University of Thessaloniki, Thessaloniki, GreeceThis 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.https://ieeexplore.ieee.org/document/10942326/Classifier combinationfusionproduct ruleensemble learningsource device identification (SDI)multimodal |
| spellingShingle | Ioannis Tsingalis Constantine Kotropoulos On Combining Deep Neural Network Classifiers for Source Device Identification IEEE Access Classifier combination fusion product rule ensemble learning source device identification (SDI) multimodal |
| title | On Combining Deep Neural Network Classifiers for Source Device Identification |
| title_full | On Combining Deep Neural Network Classifiers for Source Device Identification |
| title_fullStr | On Combining Deep Neural Network Classifiers for Source Device Identification |
| title_full_unstemmed | On Combining Deep Neural Network Classifiers for Source Device Identification |
| title_short | On Combining Deep Neural Network Classifiers for Source Device Identification |
| title_sort | on combining deep neural network classifiers for source device identification |
| topic | Classifier combination fusion product rule ensemble learning source device identification (SDI) multimodal |
| url | https://ieeexplore.ieee.org/document/10942326/ |
| work_keys_str_mv | AT ioannistsingalis oncombiningdeepneuralnetworkclassifiersforsourcedeviceidentification AT constantinekotropoulos oncombiningdeepneuralnetworkclassifiersforsourcedeviceidentification |