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Geo-DefakeHop: High-Performance Geographic Fake Image Detection

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arxiv 2110.09795 v1 pith:WYUJDTPS submitted 2021-10-19 cs.CV cs.LGeess.IV

Hong-Shuo Chen , Kaitai Zhang , Shuowen Hu , Suya You , C.-C. Jay Kuo This is my paper · ORCID
classification cs.CVcs.LGeess.IV
keywords geo-defakehopfakeimagedetectionfilterbankchannelsdecision
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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 a filter bank, Geo-DefakeHop learns the most discriminant channels and uses their soft decision scores as features. Then, Geo-DefakeHop selects a few discriminant features from each filter bank and ensemble them to make a final binary decision. Geo-DefakeHop offers a light-weight high-performance solution to fake satellite images detection. Its model size is analyzed, which ranges from 0.8 to 62K parameters. Furthermore, it is shown by experimental results that it achieves an F1-score higher than 95\% under various common image manipulations such as resizing, compression and noise corruption.

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    cs.CV 2025-06 conditional novelty 4.0 of 10

    A spatial-frequency feature fusion network with attention achieves improved accuracy on remote sensing image forgery detection and introduces a stable-diffusion-based benchmark.

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