Fake Satellite Image Detection via Parallel Subspace Learning (PSL)

Hong-Shuo Chen, Kaitai Zhang, Shuowen Hu, Suya You, C.-C. Jay Kuo

Abstract: A new method, called PSL-DefakeHop, is proposed to detect fake satellite images based on the parallel subspace learning (PSL) framework in this work. The DefakeHop method was developed previously for detection of deepfake generated faces under the successive subspace learning (SSL) framework. PSL is proposed to extract features from responses of multiple single-stage filter banks (or called PixelHops), which operate in parallel, and it improves SSL that extracts features from multi-stage cascaded filter banks. PSL has two advantages. First, PSL preserves discriminant features often lie in high-frequency channels, which are however ignored by SSL. Second, decisions from multiple filter banks can be ensembled to further improve detection accuracy. To demonstrate the effectiveness of the proposed PSL-DefakeHop method, we evaluate it on the UW Fake Satellite Image dataset and observe perfect classification performance (i.e., 100% F1 score, precision and recall).