Abstract
Forensics still faces a serious challenge with image copy-move forgery, in which the copied source and pasted target regions exist in the same image, also known as a homogeneous forgery. In the past decade, numerous copy-move forgery detection (CMFD) methods have attempted to resolve this issue. However, the traditional keypoint-based and block-based methods have certain insurmountable deficiencies, such as the inability to smooth out regions and the lack of scaling invariance. Since the introduction of deep neural networks (DNNs) in the CMFD scheme, researchers have been able to overcome the defects of the traditional hand-crafted methods and obtain promising results. Using DNNs as a reference, this paper proposes a coarse-to-fine spatial-channel-boundary attention network (SCBAN) which is more suited to CMFD. SCBAN consists of three sub-networks, namely, feature extraction, coarse forgery identification, and fine forgery identification modules. First, CondenseNet will serve as SCBAN's backbone for feature extraction. Next, we present a dual-correlation-attention module for parallel fusion, as well as a nearest-correlation matching module for coarse forgery identification. In addition, we propose a boundary refinement attention module for fine forgery identification. We have conducted numerous experiments on IMD, CoMoFoD, and CMHD benchmarks to show that our SCBAN can achieve the best performance and robustness, compared to the existing DNN CMFD. In addition, unlike the well-designed hand-crafted methods which achieve good performance in a specific dataset, our SCBAN can maintain its scalability to achieve good performance on multiple benchmark datasets.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
The datasets generated during and/or analysed during the current study are not publicly available due to the right to portrait of the person photographed in the video but are available from the corresponding author on reasonable request.
References
Bay H, Tuytelaars T, Van Gool L (2006) Surf: speeded up robust features. In: European conference on computer vision, pp 404–417. Springer
Bian J, Lin W-Y, Matsushita Y, Yeung S-K, Nguyen T-D, Cheng M-M (2017) Gms: grid-based motion statistics for fast, ultra-robust feature correspondence. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4181–4190
Bi X, Pun C-M (2018) Fast copy-move forgery detection using local bidirectional coherency error refinement. Pattern Recogn 81:161–175
Chen B, Tan W, Coatrieux G, Zheng Y, Shi YQ (2020) A serial image copy-move forgery localization scheme with source/target distinguishment. IEEE Trans Multim 23:3506–3517
Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Trans Inf Forensics Secur 7(6):1841–1854
Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copy–move forgery detection. IEEE Trans Inf Forensics Secur 10(11):2284–2297
Dai X, et al. (2021) General instance distillation for object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 7842–7851
Deng J-H, Yang J-X, Weng S-W, Gu G-S, Li Z (2018) Copy-move forgery detection robust to various transformation and degradation attacks. KSII Trans Internet Inf Syst 12(9):4467–4486
Emam M, Han Q, Niu XM (2016) PCET based copy-move forgery detection in images under geometric transforms. Multimed Tools Appl 75(18):11513–11527
Fu J, Liu J, Tian H-J, Li Y, Bao Y-J, Fang Z-W, Lu H-Q (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 3146–3154
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708
Huang G, Liu S, Van der Maaten L, Weinberger KQ (2018) Condensenet: an efficient densenet using learned group convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2752–2761
Li Y (2013) Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Sci Int 224(1):59–67
Li J, Li X, Yang B, Sun X (2014) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518
Li Y, Zhou J (2019) Fast and effective image copy-move forgery detection via hierarchical feature point matching. IEEE Trans Inf Forensics Secur 14(5):1307–1322
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput vis 60(2):91–110
Popescu A, Farid H (2004) Exposing digital forgeries by detecting duplicated image region [Technical Report]. 2004–515. Hanover, Department of Computer Science, Dartmouth College. USA, p 32
Pun C-M, Chung J-L (2018) A two-stage localization for copy-move forgery detection. Inf Sci 463–464:33–55
Pun C-M, Yuan X-C, Bi X-L (2015) Image forgery detection using adaptive oversegmentation and feature point matching. IEEE Trans Inf Forensics Secur 10(8):1705–1716
Rao Y, Ni J (2016) A deep learning approach to detection of splicing and copy-move forgeries in images. In: 2016 IEEE international workshop on information forensics and security (WIFS), pp. 1–6: IEEE
Ryu S-J, Lee M-J, Lee H-K (2010) Detection of copy-rotate-move forgery using Zernike moments. In: International workshop on information hiding, pp 51–65. Springer
Shivakumar BL, Baboo SS (2011) Detection of region duplication forgery in digital images using SURF. Int J Comput Sci Issues 8(4):199–205
Silva E, Carvalho T, Ferreira A, Rocha A (2015) Going deeper into copy-move forgery detection: exploring image telltales via multi-scale analysis and voting processes. J vis Commun Image Represent 29:16–32
Tralic D, Zupancic I, Grgic S, Grgic M (2013) CoMoFoD—new database for copy-move forgery detection. In: Proceedings ELMAR-2013, pp 49–54: IEEE
Wang Y, et al. (2021) End-to-end video instance segmentation with transformers. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 8741–8750
Wang K, He J, Zhang L (2019) Attention-based convolutional neural network for weakly labeled human activities’ recognition with wearable sensors. IEEE Sens J 19(17):7598–7604
Wu Y, Abd-Almageed W, Natarajan P (2018) BusterNet: detecting copy-move image forgery with source/target localization. In: Proceedings of the European conference on computer vision (ECCV), pp. 168–184
Yang J, Liang Z, Gan Y, Zhong J (2021) A novel copy-move forgery detection algorithm via two-stage filtering. Digital Signal Process 113:103032
Zhong J-L, Pun C-M (2019) An end-to-end dense-inceptionnet for image copy-move forgery detection. IEEE Trans Inf Forensics Secur 15:2134–2146
Zhong J-L, Pun C-M (2020) Two-pass hashing feature representation and searching method for copy-move forgery detection. Inf Sci 512:675–692
Zhong J, Gan Y, Young J, Huang L, Lin P (2017) A new block-based method for copy move forgery detection under image geometric transforms. Multimed Tools Appl 76(13):14887–14903
Zhong JL, Pun CM, Gan YF (2020) Dense moment feature index and best match algorithms for video copy-move forgery detection. Inf Sci 537:184–202
Zhuang P, Li H, Tan S, Li B, Huang J (2021) Image tampering localization using a dense fully convolutional network. IEEE Trans Inf Forensics Secur 16:2986–2999
Zhu Y, Chen C, Yan G, Guo Y, Dong Y (2020) AR-Net: adaptive attention and residual refinement network for copy-move forgery detection. IEEE Trans Ind Informat 16(10):6714–6722
Funding
This work was partly supported by Guangdong basic and applied basic research foundation under Grant No. 2020A151501783 (2020A1515010700), the 2021 Innovation team of scientific research platform of universities in Guangdong Province Grant, No. 2021KCXTD053, the 2021 Guangdong scientific research capacity improvement project of key construction disciplines, No. 2021ZDJS058, and the Innovative young talents foundation in higher education of Guangdong, No. 2021KQNCX164.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Ji-Xiang Yang], [Yan-Fen Gan], [Lian Huang] and [Hua Zeng]. The first draft of the manuscript was written by [Jun-Liu Zhong] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhong, JL., Yang, JX., Gan, YF. et al. Coarse-to-fine spatial-channel-boundary attention network for image copy-move forgery detection. Soft Comput 26, 11461–11478 (2022). https://doi.org/10.1007/s00500-022-07432-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-022-07432-x