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http://dx.doi.org/10.14372/IEMEK.2022.17.6.309

Dual Branched Copy-Move Forgery Detection Network Using Rotation Invariant Energy in Wavelet Domain  

Jun Young, Park (Pusan National University)
Sang In, Lee (Pusan National University)
Il Kyu, Eom (Pusan National University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.17, no.6, 2022 , pp. 309-317 More about this Journal
Abstract
In this paper, we propose a machine learning-based copy-move forgery detection network with dual branches. Because the rotation or scaling operation is frequently involved in copy-move forger, the conventional convolutional neural network is not effectively applied in detecting copy-move tampering. Therefore, we divide the input into rotation-invariant and scaling-invariant features based on the wavelet coefficients. Each of the features is input to different branches having the same structure, and is fused in the combination module. Each branch comprises feature extraction, correlation, and mask decoder modules. In the proposed network, VGG16 is used for the feature extraction module. To check similarity of features generated by the feature extraction module, the conventional correlation module used. Finally, the mask decoder model is applied to develop a pixel-level localization map. We perform experiments on test dataset and compare the proposed method with state-of-the-art tampering localization methods. The results demonstrate that the proposed scheme outperforms the existing approaches.
Keywords
Copy-move forgery; Copy-move forgery localization; Dual branched network; Convolutional neural network; Rotation-invariant; Wavelet transform;
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