Bibliographic Details
| Title: |
CNN-Based Copy-Move Forgery Detection Using Rotation-Invariant Wavelet Feature |
| Authors: |
Sang In Lee, Jun Young Park, Il Kyu Eom |
| Source: |
IEEE Access, Vol 10, Pp 106217-106229 (2022) |
| Publisher Information: |
IEEE, 2022. |
| Publication Year: |
2022 |
| Collection: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| Subject Terms: |
Copy-move forgery, copy-move forgery localization, convolutional neural network, rotation-invariant, stationary wavelet transform, root-mean squared energy, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/9911656/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3212069 |
| Access URL: |
https://doaj.org/article/27970196d67b4d38a745ca7d946c2ba8 |
| Accession Number: |
edsdoj.27970196d67b4d38a745ca7d946c2ba8 |
| Database: |
Directory of Open Access Journals |
