Anomaly-Based Manipulation Detection in Satellite Images

Janos Horvath, David Guera, Sri Kalyan Yarlagadda, Paolo Bestagini, Fengqing Maggie Zhu, Stefano Tubaro, Edward J. Delp; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 62-71

Abstract


Satellite overhead imagery can be easily acquired and shared. The integrity of these type of images cannot longer be assumed, due to availability of sophisticated classical and machine learning based image manipulation tools. In this paper we proposed a deep learning based method for detecting and localizing splicing manipulations in overhead images. Our method uses recent advances in anomaly detection and does not require any prior knowledge of the type of manipulations that an adversary could insert in the satellite imagery. We compare our method against robust satellite-based manipulation detection approaches. We show that our proposed technique outperforms all previous methods, especially in detecting small-sized manipulations.

Related Material


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[bibtex]
@InProceedings{Horvath_2019_CVPR_Workshops,
author = {Horvath, Janos and Guera, David and Kalyan Yarlagadda, Sri and Bestagini, Paolo and Maggie Zhu, Fengqing and Tubaro, Stefano and Delp, Edward J.},
title = {Anomaly-Based Manipulation Detection in Satellite Images},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}