Social network identification through image classification with CNN

Amerini, Irene, Li, Chang-Tsun and Caldelli, Roberto 2019, Social network identification through image classification with CNN, IEEE access, vol. 7, pp. 35264-35273, doi: 10.1109/ACCESS.2019.2903876.

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Title Social network identification through image classification with CNN
Author(s) Amerini, Irene
Li, Chang-TsunORCID iD for Li, Chang-Tsun orcid.org/0000-0003-4735-6138
Caldelli, Roberto
Journal name IEEE access
Volume number 7
Start page 35264
End page 35273
Total pages 10
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2019
ISSN 2169-3536
Keyword(s) Source identification
multimedia forensics
image provenance
CNN
noise residual
cyber-security
Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
Summary Identification of the source social network based on the downloaded images is an important multimedia forensic task with significant cybersecurity implications in light of the sheer volume of images and videos shared across various social media platforms. Such a task has been proved possible by exploiting distinctive traces embedded in image content by social networks (SNs). To further advance the development of this area, we propose a novel framework, called FusionNET, that integrates two established convolutional neural networks (CNNs), with the former (named 1D-CNN) learning discriminative features from the histogram of discrete cosine transform coefficients and the latter (named 2D-CNN) inferring unique attributes from the sensor-related noise residual of the images in question. The separately learned features are then fused by the FusionNET to inform the ensuing source identification or source-oriented image classification component. A series of experiments were conducted on a number of image datasets across various SNs and instant messaging apps to validate the feasibility of the FusionNET also in comparison with the performance of the 1D-CNN and 2D-CNN. The encouraging results were observed.
Language eng
DOI 10.1109/ACCESS.2019.2903876
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2019, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30121297

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