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A MULTI-SCALE CONTENT-INSENSITIVE FUSION CNN FOR SOURCE SOCIAL NETWORK IDENTIFICATION

conference contribution
posted on 2023-02-21, 01:52 authored by Manisha, Chang-Tsun LiChang-Tsun Li, KA Kotegar
Identification of source social networks of images based on the traces left on images by such platforms is a crucial task in image forensics. The existing techniques provide successful solutions to such a problem. However, we show that the state-of-the-art techniques are adversely affected due to the leaking side-channel information from scene details that convolutional neural networks (CNNs) are prone to exploit. Thus, highly correlated scene details in the training and test sets lead to overestimation of the performance. To address this problem, we develop a data-driven system by parallelizing three CNNs having kernels with different sizes that benefit from learning more relevant forensic traces making the model less susceptible to scene content. The experimental results achieved by the proposed model either trained on images with or without scene overlap show that there is no influence of scene content in the feature learning of the proposed method.

History

Pagination

2981-2985

Location

Bordeaux, France

Start date

2022-10-16

End date

2022-10-19

ISSN

1522-4880

ISBN-13

9781665496209

Language

English

Publication classification

E1 Full written paper - refereed

Title of proceedings

ICIP 2022 : Proceedings of the IEEE International Conference on Image Processing 2022

Event

IEEE International Conference on Image Processing. (2022 : Bordeaux, France)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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