CNN-Based Copy-Move Forgery Detection Using Rotation-Invariant Wavelet Feature

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Abstract

This paper introduces a machine learning based copy-move forgery (CMF) localization method. The basic convolutional neural network cannot be applied to CMF detection because CMF frequently involves rotation transformation. Therefore, we propose a rotation-invariant feature based on the root-mean squared energy using high-frequency wavelet coefficients. Instead of using three color image channels, two-scale energy features and low-frequency subband image are fed into the conventional VGG16 network. A correlation module is used by employing small feature patches generated by the VGG16 network to obtain the possible copied and moved patch pairs. The all-to-all similarity score is computed using the correlation module. To generate the final binary localization map, a simplified mask decoder module is introduced, which is composed of two simple bilinear upsampling and two batch-normalized-inception-based mask deconvolution followed by bilinear upsampling. We perform experiments on four test datasets and compare the proposed method with state-of-the-art tampering localization methods. The results demonstrate that the proposed scheme outperforms the existing approaches.

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Lee, S. I., Park, J. Y., & Eom, I. K. (2022). CNN-Based Copy-Move Forgery Detection Using Rotation-Invariant Wavelet Feature. IEEE Access, 10, 106217–106229. https://doi.org/10.1109/ACCESS.2022.3212069

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