Misinformation on the Internet and social media, ranging from fake news to fake multimedia such as images and videos, is a significant threat to our society. Effective misinformation detection has become a research focus, driven by commercial and government funding. With the fast-growing deep learning techniques, real-looking fake images can be easily generated using generative adversarial networks (GANs). The problem of fake satellite images detection was recently introduced. Fake satellite images could be generated with the intention of hiding important infrastructure and/or creating fake buildings to deceive others. Although it may be feasible to check whether these images are real or fake using another satellite, the cost is high. Furthermore, the general public and media do not have the proper resource to verify the authenticity of fake satellite images. Consequently, fake satellite images pose serious challenges to our society, as recognized by government organizations concerned about the political and military implications of such technology. Handcrafted features were used for fake satellite image detection, and its best detection performance measured by the F-1 score is 87%. 

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 the 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).

— by Hong-Shuo Chen