APSIPA Transactions on Signal and Information Processing > Vol 13 > Issue 3

Geo-DefakeHop: High-Performance Geographic Fake Image Detection

Hong-Shuo Chen, University of Southern California, USA, hongshuo@usc.edu , Kaitai Zhang, University of Southern California, USA, Shuowen Hu, DEVCOM Army Research Laboratory, USA, Suya You, DEVCOM Army Research Laboratory, USA, C.-C. Jay Kuo, University of Southern California, USA
Suggested Citation
Hong-Shuo Chen, Kaitai Zhang, Shuowen Hu, Suya You and C.-C. Jay Kuo (2024), "Geo-DefakeHop: High-Performance Geographic Fake Image Detection", APSIPA Transactions on Signal and Information Processing: Vol. 13: No. 3, e200. http://dx.doi.org/10.1561/116.00000072

Publication Date: 22 Apr 2024
© 2024 H.-S. Chen, K. Zhang, S. Hu, S. You and C.-C.J. Kuo
Geo Artificial Intelligence (GeoAI)Parallel Subspace Learning (PSL)Generative Adversarial Network (GAN)


Open Access

This is published under the terms of CC BY-NC.

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In this article:
Related Work 
Geo-DefakeHop Method 
Conclusion and Future Work 


A robust fake satellite image detection method, called Geo-DefakeHop, is proposed in this work. Geo-DefakeHop is developed based on the parallel subspace learning (PSL) methodology. PSL maps the input image space into several feature subspaces using multiple filter banks. By exploring response differences of different channels between real and fake images for filter banks, Geo-DefakeHop learns the most discriminant channels based on the validation dataset, uses their soft decision scores as features, and ensemble them to get the final binary decision. Geo-DefakeHop offers a light-weight high-performance solution to fake satellite images detection. The model size of Geo-DefakeHop ranges from 0.8K to 62K parameters depending on different hyper-parameter settings. Experimental results show that Geo-DefakeHop achieves F1-scores higher than 95% under various common image manipulations such as resizing, compression and noise corruption.



APSIPA Transactions on Signal and Information Processing Special Issue - Advanced Machine Learning Techniques for Remote Sensing: Algorithms and Applications
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