TY - GEN
T1 - RA-Net
T2 - 32nd International Conference on Artificial Neural Networks, ICANN 2023
AU - Zhao, Kaiqi
AU - Yuan, Xiaochen
AU - Xie, Zhiyao
AU - Huang, Guoheng
AU - Feng, Li
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - To reduce the difficulty of image forensics on forgery images, in this paper, we present an efficient end-to-end deep learning approach using Residual Structure and Attention Mechanism (RA-Net) for image copy-move forgery detection (CMFD). The RA-Net can locate the forged areas and corresponding genuine areas, and it is composed of two modules, Residual Feature Extraction module (RFEM) and Feature Matching & Up-sampling module (FMUM). RFEM is designed to extract deep feature maps, which enriches the combination of gradient information and attention mechanism that focuses the attention of RA-Net to the forged areas. The FMUM assists RA-Net is used to detect copy-move forgery areas and return the previous output to the size of the input image for analysis and visualization of the results. Furthermore, we create a RANet-CMFD dataset for the training, the way to generate RA-Net-CMFD dataset could help solve the problem of not having enough dataset in some research areas. Otherwise, comparison results show that our model can achieve satisfied performance on CoMoFoD dataset at the pixel level, and performs superior than the compared methods.
AB - To reduce the difficulty of image forensics on forgery images, in this paper, we present an efficient end-to-end deep learning approach using Residual Structure and Attention Mechanism (RA-Net) for image copy-move forgery detection (CMFD). The RA-Net can locate the forged areas and corresponding genuine areas, and it is composed of two modules, Residual Feature Extraction module (RFEM) and Feature Matching & Up-sampling module (FMUM). RFEM is designed to extract deep feature maps, which enriches the combination of gradient information and attention mechanism that focuses the attention of RA-Net to the forged areas. The FMUM assists RA-Net is used to detect copy-move forgery areas and return the previous output to the size of the input image for analysis and visualization of the results. Furthermore, we create a RANet-CMFD dataset for the training, the way to generate RA-Net-CMFD dataset could help solve the problem of not having enough dataset in some research areas. Otherwise, comparison results show that our model can achieve satisfied performance on CoMoFoD dataset at the pixel level, and performs superior than the compared methods.
KW - Copy-move Forgery Detection
KW - Image Forensics
KW - Residual Feature Extraction
UR - http://www.scopus.com/inward/record.url?scp=85174586865&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-44204-9_31
DO - 10.1007/978-3-031-44204-9_31
M3 - Conference contribution
AN - SCOPUS:85174586865
SN - 9783031442032
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 371
EP - 381
BT - Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings
A2 - Iliadis, Lazaros
A2 - Papaleonidas, Antonios
A2 - Angelov, Plamen
A2 - Jayne, Chrisina
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 26 September 2023 through 29 September 2023
ER -