Structural Correlation Based Method for Image Forgery Classification and Localization
Source abstract
Abstract
In the image forgery problems, previous works has been chiefly designed considering only one of two forgery types: copy-move and splicing. In this paper, we propose a scheme to handle both copy-move and splicing image forgery by concurrently classifying the image forgery types and localizing the forged regions. The structural correlations between images are employed in the forgery clustering algorithm to assemble relevant images into clusters. Then, we search for the matching of image regions inside each cluster to classify and localize tampered images. Comprehensive experiments are conducted on three datasets (MICC-600, GRIP, and CASIA 2) to demonstrate the better performance in forgery classification and localization of the proposed method in comparison with state-of-the-art methods. Further, in copy-move localization, the source and target regions are explicitly specified.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Media Forensic Detection · Image Processing Techniques and Applications · Cell Image Analysis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Digital Contents, Sejong University, Seoul 05006, Korea
Department of Software, Sejong University, Seoul 05006, Korea
Department of Computer Education, Sungkyunkwan University, Seoul 03063, Korea
Funding
Ministry of Science and ICT, South Korea · National Research Foundation of Korea
References (44)
Show source-provided references
1. Pham, N.T., Lee, J.W., Kwon, G.-R., and Park, C.-S. (2019). Hybrid Image-Retrieval Method for Image-Splicing Validation. Symmetry, 11.10.3390/sym11010083 ↗
2. Quaternion pseudo-Zernike moments combining both of RGB information and depth information for color image splicing detection10.1016/j.jvcir.2017.08.011 ↗
3. Digital image splicing detection based on Markov features in DCT and DWT domain10.1016/j.patcog.2012.05.014 ↗
4. Efficient image splicing detection algorithm based on Markov features10.1007/s11042-018-6792-9 ↗
5. A Novel Framework for Trash Classification Using Deep Transfer Learning10.1109/ACCESS.2019.2959033 ↗
6. Le, T., Vo, M.T., Kieu, T., Hwang, E., Rho, S., and Baik, S. (2020). Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building. Sensors, 20.10.3390/s20092668 ↗
7. Large-scale evaluation of splicing localization algorithms for web images10.1007/s11042-016-3795-2 ↗
8. Bondi, L., Lameri, S., Güera, D., Bestagini, P., Delp, E., and Tubaro, S. (2017, January 21–26). Tampering Detection and Localization Through Clustering of Camera-Based CNN Features. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.10.1109/CVPRW.2017.232 ↗
9. Image Manipulation Detection and Localization Based on the Dual-Domain Convolutional Neural Networks10.1109/ACCESS.2018.2883588 ↗
10. An Improved Splicing Localization Method by Fully Convolutional Networks10.1109/ACCESS.2018.2880433 ↗
11. Detecting Image Splicing Based on Noise Level Inconsistency10.1007/s11042-016-3660-3 ↗
12. Forensic Similarity for Digital Images10.1109/TIFS.2019.2924552 ↗
13. Image splicing localization using PCA-based noise level estimation10.1007/s11042-016-3712-8 ↗
14. A PUF-Based Data-Device Hash for Tampered Image Detection and Source Camera Identification10.1109/TIFS.2019.2926777 ↗
15. Gradient-Based Illumination Description for Image Forgery Detection10.1109/TIFS.2019.2935913 ↗
16. Blind forensics of image gamma transformation and its application in splicing detection10.1016/j.jvcir.2018.05.020 ↗
17. Rotation and scale invariant upsampled log-polar fourier descriptor for copy-move forgery detection10.1007/s11042-016-3575-z ↗
18. Wu, Y., Abd-Almageed, W., and Natarajan, P. (2018, January 8–14). BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01231-1_11 ↗
19. Fast and robust copy-move forgery detection based on scale-space representation10.1007/s11042-017-5248-y ↗
20. Fractional Quaternion Zernike Moments for Robust Color Image Copy-Move Forgery Detection10.1109/ACCESS.2018.2871952 ↗
21. An End-to-End Dense-InceptionNet for Image Copy-Move Forgery Detection10.1109/TIFS.2019.2957693 ↗
22. Wu, Y., Abd-Almageed, W., and Natarajan, P. (2018, January 12–15). Image Copy-Move Forgery Detection via an End-to-End Deep Neural Network. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.10.1109/WACV.2018.00211 ↗
23. Cozzolino, D., Poggi, G., and Verdoliva, L. (2013, January 6–10). Copy-move forgery detection based on PatchMatch. Proceedings of the IEEE International Conference on Image Processing (ICIP), Beijing, China.10.1109/ICIP.2014.7026075 ↗
24. Efficient Dense-Field Copy–Move Forgery Detection10.1109/TIFS.2015.2455334 ↗
25. Copy-move forgery detection and localization by means of robust clustering with J-Linkage10.1016/j.image.2013.03.006 ↗
26. An improved method for SIFT-based copy–move forgery detection using non-maximum value suppression and optimized J-Linkage10.1016/j.image.2017.05.010 ↗
27. Fast and Effective Image Copy-Move Forgery Detection via Hierarchical Feature Point Matching10.1109/TIFS.2018.2876837 ↗
28. Fast copy-move forgery detection using local bidirectional coherency error refinement10.1016/j.patcog.2018.03.028 ↗
29. Segmentation-Based Image Copy-Move Forgery Detection Scheme10.1109/TIFS.2014.2381872 ↗
30. Fusion of block and keypoints based approaches for effective copy-move image forgery detection10.1007/s11045-016-0416-1 ↗
31. Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching10.1109/TIFS.2015.2423261 ↗
32. Fractional quaternion cosine transform and its application in color image copy-move forgery detection10.1007/s11042-018-6595-z ↗
33. Distinctive Image Features from Scale-Invariant Keypoints10.1023/B:VISI.0000029664.99615.94 ↗
34. Graph-Based Image Matching for Indoor Localization10.3390/make1030046 ↗
35. Bag of ARSRG Words (BoAW)10.3390/make1030050 ↗
36. Tolias, G., Avrithis, Y., and Jégou, H. (2013, January 1–8). To Aggregate or Not to aggregate: Selective Match Kernels for Image Search. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, NSW, Australia.10.1109/ICCV.2013.177 ↗
37. Arandjelović, R., and Zisserman, A. (2014, January 1–5). DisLocation: Scalable Descriptor Distinctiveness for Location Recognition. Proceedings of the Asian Conference on Computer Vision (ACCV), Singapore.10.1007/978-3-319-16817-3_13 ↗
38. Jegou, H., Douze, M., and Schmid, C. (2008, January 12–18). Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search. Proceedings of the European Conference on Computer Vision (ECCV), Marseille, France.10.1007/978-3-540-88682-2_24 ↗
39. Sattler, T., Havlena, M., Schindler, K., and Pollefeys, M. (2016, January 27–30). Large-Scale Location Recognition and the Geometric Burstiness Problem. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.175 ↗
40. Jegou, H., Douze, M., and Schmid, C. (2009, January 20–25). On the burstiness of visual elements. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, USA.10.1109/CVPRW.2009.5206609 ↗
41. Understanding inverse document frequency: On theoretical arguments for IDF10.1108/00220410410560582 ↗
42. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography10.1145/358669.358692 ↗
43. Gonzalez, R.C., and Woods, R.E. (2008). Image Compression. Digital Image Processing, Pearson Prentice Hall. [3rd ed.].
44. Dong, J., Wang, Y., 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 (ChinaSIP), Beijing, China.10.1109/ChinaSIP.2013.6625374 ↗