  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.
