Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network
Source abstract
Abstract
Splicing is the most common operation in image forgery, where the tampered background regions are imported from different images. Illumination maps are inherent attribute of images and provide significant clues when searching for splicing locations. This paper proposes an end-to-end dual-stream network for splicing location, where the illumination stream, which includes Grey-Edge (GE) and Inverse-Intensity Chromaticity (IIC), extract the inconsistent features, and the image stream extracts the global unnatural tampered features. The dual-stream feature in our network is fused through Multiple Feature Pyramid Network (MFPN), which contains richer context information. Finally, a Cluster Region Proposal Network (C-RPN) with spatial attention and an adaptive cluster anchor are proposed to generate potential tampered regions with greater retention of location information. Extensive experiments, which were evaluated on the NIST16 and CASIA standard datasets, show that our proposed algorithm is superior to some state-of-the-art algorithms, because it achieves accurate tampered locations at the pixel level, and has great robustness in post-processing operations, such as noise, blur and JPEG recompression.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Media Forensic Detection · Cell Image Analysis Techniques · Image Processing Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 510900, China
College of Computer Science and Technology, Jilin University, Changchun 130012, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Hebei Province · Sci-tech Research Projects of Higher Education of Hebei Province, China
References (35)
Show source-provided references
1. Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts10.1109/TIFS.2012.2202227 ↗
2. A pictures worth
3. Using noise inconsistencies for blind image forensics10.1016/j.imavis.2009.02.001 ↗
4. Pixel and edge based illuminant color estimation for image forgery detection10.1016/j.procs.2015.02.099 ↗
5. Kee, E., and Farid, H. (2010, January 12–15). Exposing digital forgeries from 3-D lighting environments. Proceedings of the 2010 IEEE International Workshop Information Forensics and Security, Seattle, WA, USA.10.1109/WIFS.2010.5711437 ↗
6. Francis, K., Gholap, S., and Bora, P.K. (March, January 28). Illuminant colour based image forensics using mismatch in human skin highlights. Proceedings of the 2014 International Conference on Communications, Kanpur, India.10.1109/NCC.2014.6811349 ↗
7. Image region forgery detection: A deep learning approach
8. Rao, Y., and Ni, J. (2016, January 4–7). A deep learning approach to detection of splicing and copy-move forgeries in images. Proceedings of the 2016 International Workshop on Information Forensics and Security, AbuDhabi, United Arab Emirates.10.1109/WIFS.2016.7823911 ↗
9. Bondi, L., Lameri, S., Guera, D., Bestagini, P., Delp, E.J., and Tubaro, S. (2017, January 21–26). Tampering detection and localization through clustering of camera-based CNN features. Proceedings of the Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.10.1109/CVPRW.2017.232 ↗
10. Wu, Y., Abd-Almageed, W., and Natarajan, P. (2017, January 23–27). Deep matching and validation network: An end-to-end solution to constrained image splicing localization and detection. Proceedings of the 25th ACM International Conference on Multimedia, Mountain View, CA, USA.10.1145/3123266.3123411 ↗
11. Adversarial Learning for Constrained Image Splicing Detection and Localization Based on Atrous Convolution10.1109/TIFS.2019.2902826 ↗
12. Constrained Image Splicing Detection and Localization with Attention-Aware Encoder-Decoder and Atrous Convolution10.1109/ACCESS.2019.2963745 ↗
13. A technique for image splicing detection using hybrid feature set10.1007/s11042-019-08480-6 ↗
14. Bi, X., Wei, Y., Xiao, B., and Li, W. (2019, January 16–17). Rru-net: The ringed residual u-net for image splicing forgery detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.10.1109/CVPRW.2019.00010 ↗
15. Image splicing forgery detection combining coarse to refined convolutional neural network and adaptive clustering10.1016/j.ins.2019.09.038 ↗
16. Image splicing detection using mask-RCNN10.1007/s11760-020-01636-0 ↗
17. Hu, X., Zhang, Z., Jiang, Z., Chaudhuri, S., Yang, Z., and Nevatia, R. (2020, January 23–28). SPAN: Spatial Pyramid Attention Network for Image Manipulation Localization. Proceedings of the European Conference on Computer Vision, Glasgow, UK.10.1007/978-3-030-58589-1_19 ↗
18. Image splicing localization using a multi-task fully convolutional network (MFCN)10.1016/j.jvcir.2018.01.010 ↗
19. Image Manipulation Detection and Localization Based on the Dual-Domain Convolutional Neural Networks10.1109/ACCESS.2018.2883588 ↗
20. Zhou, P., Han, X., Morariu, V.I., and Davis, L.S. (2018, January 18–22). Learning rich features for image manipulation detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00116 ↗
21. Wu, Y., Abd Almageed, W., and Natarajan, P. (2019, January 15–20). ManTra-net: Manipulation tracing net-work for detection and localization of image forgeries with anomalous features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00977 ↗
22. Mazaheri, G., Mithun, N.C., Bappy, J.H., and Roy-Chowdhury, A.K. (2019, January 16–17). A skip connection architecture for localization of image manipulations. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.
23. Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries10.1109/TIP.2019.2895466 ↗
24. Riess, C., and Angelopoulou, E. (2010, January 28–30). Scene illumination as an indicator of image manipulation. Proceedings of the International Workshop on Information Hiding, Calgary, AB, Canada.10.1007/978-3-642-16435-4_6 ↗
25. Illuminant-Based Transformed Spaces for Image Forensics10.1109/TIFS.2015.2506548 ↗
26. Pomari, T., Ruppert, G., Rezende, E., Rocha, A., and Carvalho, T. (2018, January 7–10). Image splicing detection through illumination inconsistencies and deep learning. Proceedings of the 2018 IEEE International Conference on Image Processing, Athens, Greece.10.1109/ICIP.2018.8451227 ↗
27. Mazumdar, A., and Bora, P.K. (2019, January 22–25). Deep learning-based classification of illumination maps for exposing face splicing forgeries in images. Proceedings of the 2019 IEEE International Conference on Image Processing, Taipei, Taiwan.10.1109/ICIP.2019.8802969 ↗
28. Edge-based color constancy10.1109/TIP.2007.901808 ↗
29. Color constancy through inverse-intensity chromaticity space10.1364/JOSAA.21.000321 ↗
30. Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5–9). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.10.1007/978-3-319-24574-4_28 ↗
31. Kong, T., Yao, A., Chen, Y., and Sun, F. (2016, January 27–30). HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.98 ↗
32. Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21–26). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.106 ↗
33. (2020, September 10). Nist Nimble 2016 Datasets, Available online: https://www.nist.gov/itl/iad/mig/nimble-challenge-2017-evaluation/.
34. Dong, J., Wang, W., and Tan, T. (2013, January 6–10). Casia image tampering detection evaluation database. Proceedings of the 2013 IEEE China Summit and International Conference on Signal and Information Processing, Beijing, China.10.1109/ChinaSIP.2013.6625374 ↗
35. Lin, T., Maire, M., Belonge, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C.L. (2014, January 6–12). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.10.1007/978-3-319-10602-1_48 ↗