{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T07:15:10Z","timestamp":1769584510857,"version":"3.49.0"},"reference-count":29,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T00:00:00Z","timestamp":1569888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. U1703261 and No. 61871258"],"award-info":[{"award-number":["No. U1703261 and No. 61871258"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Due to the wide availability of the tools used to produce manipulated images, a large number of digital images have been tampered with in various media, such as newspapers and social networks, which makes the detection of tampered images particularly important. Therefore, an image manipulation detection algorithm leveraged by the Faster Region-based Convolutional Neural Network (Faster R-CNN) model combined with edge detection was proposed in this paper. In our algorithm, first, original tampered images and their detected edges were sent into symmetrical ResNet101 networks to extract tampering features. Then, these features were put into the Region of Interest (RoI) pooling layer. Instead of the RoI max pooling approach, the bilinear interpolation method was adopted to obtain the RoI region. After the RoI features of original input images and edge feature images were sent into bilinear pooling layer for feature fusion, tampering classification was performed in fully connection layer. Finally, Region Proposal Network (RPN) was used to locate forgery regions. Experimental results on three different image manipulation datasets show that our proposed algorithm can detect tampered images more effectively than other existing image manipulation detection algorithms.<\/jats:p>","DOI":"10.3390\/sym11101223","type":"journal-article","created":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T11:11:16Z","timestamp":1569928276000},"page":"1223","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Developing an Image Manipulation Detection Algorithm Based on Edge Detection and Faster R-CNN"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2479-0751","authenticated-orcid":false,"given":"Xiaoyan","family":"Wei","sequence":"first","affiliation":[{"name":"College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yirong","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangmin","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuifa","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China"},{"name":"Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhang, H., and Wang, C. (2018). A Robust Image Watermarking Technique Based on DWT, APDCBT, and SVD. Symmetry, 10.","DOI":"10.3390\/sym10030077"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hou, X., Min, L., and Yang, H. (2018). A Reversible Watermarking Scheme for Vector Maps Based on Multilevel Histogram Modification. Symmetry, 10.","DOI":"10.3390\/sym10090397"},{"key":"ref_3","unstructured":"Schneider, M., and Chang, S.F. (1996, January 19). A robust content based digital signature for image authentication. Proceedings of the 3rd IEEE International Conference on Image Processing, Lausanne, Switzerland."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ye, S., Sun, Q., and Chang, E.C. (2007, January 2\u20135). Detecting Digital Image Forgeries by Measuring Inconsistencies of Blocking Artifact. Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, Beijing, China.","DOI":"10.1109\/ICME.2007.4284574"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1016\/j.sigpro.2009.03.025","article-title":"Passive detection of doctored JPEG image via block artifact grid extraction","volume":"89","author":"Li","year":"2009","journal-title":"Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhu, N., Shen, J., and Niu, X. (2019). Double JPEG Compression Detection Based on Noise-Free DCT Coefficients Mixture Histogram Model. Symmetry, 11.","DOI":"10.3390\/sym11091119"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1109\/TIFS.2011.2170836","article-title":"Detection of Nonaligned Double JPEG Compression Based on Integer Periodicity Maps","volume":"7","author":"Bianchi","year":"2012","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1016\/j.imavis.2009.02.001","article-title":"Using noise inconsistencies for blind image forensics","volume":"27","author":"Mahdian","year":"2009","journal-title":"Image Vis. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1007\/s11263-013-0688-y","article-title":"Exposing Region Splicing Forgeries with Blind Local Noise Estimation","volume":"110","author":"Lyu","year":"2014","journal-title":"Int. J. Comput. Vis."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1109\/TIFS.2012.2202227","article-title":"Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts","volume":"7","author":"Ferrara","year":"2012","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rao, Y., and Ni, J. (2016, January 4\u20137). A deep learning approach to detection of splicing and copy-move forgeries in images. Proceedings of the 2016 IEEE International Workshop on Information Forensics and Security (WIFS), Abu Dhabi, UAE.","DOI":"10.1109\/WIFS.2016.7823911"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rota, P., Sangineto, E., Conotter, V., and Pramerdorfer, C. (2016, January 4\u20138). Bad teacher or unruly student: Can deep learning say something in Image Forensics analysis?. Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR), Canc\u00fan, Mexico.","DOI":"10.1109\/ICPR.2016.7900012"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1109\/TIFS.2018.2825953","article-title":"Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection","volume":"13","author":"Bayar","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bappy, J.H., Roy-Chowdhury, A.K., Bunk, J., Nataraj, L., and Manjunath, B.S. (2017, January 22\u201329). Exploiting Spatial Structure for Localizing Manipulated Image Regions. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.532"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhou, P., Han, X., Morariu, V.I., and Davis, L.S. (2018, January 18\u201323). Learning Rich Features for Image Manipulation Detection. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00116"},{"key":"ref_17","first-page":"187","article-title":"Theory of Edge Detection","volume":"207","author":"Marr","year":"1980","journal-title":"Proc. R. Soc. Lond. Ser. B Biol. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/0734-189X(90)90004-F","article-title":"Refining edges detected by a LoG operator","volume":"51","author":"Ulupinar","year":"1990","journal-title":"Comput. Vis. Graph. Image Process."},{"key":"ref_19","first-page":"15","article-title":"Object enhancement and extraction","volume":"10","author":"Prewitt","year":"1970","journal-title":"Pict. Process. Psychopictorics"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.jvcir.2018.01.010","article-title":"Image Splicing Localization Using a Multi-Task Fully Convolutional Network (MFCN)","volume":"51","author":"Salloum","year":"2018","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1109\/TIFS.2012.2190402","article-title":"Rich Models for Steganalysis of Digital Images","volume":"7","author":"Fridrich","year":"2012","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Girshick, R.B. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_24","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the ICLR 2015: International Conference on Learning Representations 2015, San Diego, CA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., RoyChowdhury, A., and Maji, S. (2015, January 7\u201313). Bilinear CNN Models for Fine-Grained Visual Recognition. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.170"},{"key":"ref_26","unstructured":"(2018, October 30). Nist Nimble 2016 Datasets, Available online: https:\/\/mig.nist.gov\/NC2017\/Resources.html."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hsu, Y.F.J., and Chang, S.F. (2006, January 1). Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency. Proceedings of the 2006 IEEE International Conference on Multimedia and Expo, Toronto, ON, Canada.","DOI":"10.1109\/ICME.2006.262447"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Dong, J., Wang, W., and Tan, T. (2013, January 6\u201310). CASIA Image Tampering Detection Evaluation Database. Proceedings of the 2013 IEEE China Summit and International Conference on Signal and Information Processing, Beijing, China.","DOI":"10.1109\/ChinaSIP.2013.6625374"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4801","DOI":"10.1007\/s11042-016-3795-2","article-title":"Large-scale evaluation of splicing localization algorithms for web images","volume":"76","author":"Zampoglou","year":"2017","journal-title":"Multimed. Tools Appl."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/10\/1223\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:26:41Z","timestamp":1760189201000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/10\/1223"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,1]]},"references-count":29,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["sym11101223"],"URL":"https:\/\/doi.org\/10.3390\/sym11101223","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,1]]}}}