Show simple item record

dc.contributor.authorYarlagadda, S.
dc.contributor.authorGüera, D.
dc.contributor.authorBestagini, P.
dc.contributor.authorZhu, Maggie
dc.contributor.authorTubaro, S.
dc.contributor.authorDelp, E.
dc.date.accessioned2018-12-13T09:14:14Z
dc.date.available2018-12-13T09:14:14Z
dc.date.created2018-12-12T02:47:14Z
dc.date.issued2018
dc.identifier.citationYarlagadda, S. and Güera, D. and Bestagini, P. and Zhu, M. and Tubaro, S. and Delp, E. 2018. Satellite image forgery detection and localization using GAN and One-Class classifier. IS and T International Symposium on Electronic Imaging Science and Technology. Part F138662.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/72717
dc.identifier.doi10.2352/ISSN.2470-1173.2018.07.MWSF-214
dc.description.abstract

© 2018, Society for Imaging Science and Technology. Current satellite imaging technology enables shooting highresolution pictures of the ground. As any other kind of digital images, overhead pictures can also be easily forged. However, common image forensic techniques are often developed for consumer camera images, which strongly differ in their nature from satellite ones (e.g., compression schemes, post-processing, sensors, etc.). Therefore, many accurate state-of-the-art forensic algorithms are bound to fail if blindly applied to overhead image analysis. Development of novel forensic tools for satellite images is paramount to assess their authenticity and integrity. In this paper, we propose an algorithm for satellite image forgery detection and localization. Specifically, we consider the scenario in which pixels within a region of a satellite image are replaced to add or remove an object from the scene. Our algorithm works under the assumption that no forged images are available for training. Using a generative adversarial network (GAN), we learn a feature representation of pristine satellite images. A one-class support vector machine (SVM) is trained on these features to determine their distribution. Finally, image forgeries are detected as anomalies. The proposed algorithm is validated against different kinds of satellite images containing forgeries of different size and shape.

dc.titleSatellite image forgery detection and localization using GAN and One-Class classifier
dc.typeJournal Article
dcterms.source.volumePart F138662
dcterms.source.issn2470-1173
dcterms.source.titleIS and T International Symposium on Electronic Imaging Science and Technology
curtin.departmentSchool of Public Health
curtin.accessStatusFulltext not available


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record