Deepfake satellite imagery detection with multi-attention and super resolution

Deepfake satellite imagery detection is a crucial task in the era of digital deception, as the ability to manipulate and generate fake satellite images poses significant risks and negative impacts, such as disguising military activities, spreading mis-information, and undermining the trust in surveillance systems. To maintain the integrity of satellite data, we propose a novel approach detecting forged satellite images by leveraging novel technologies such as multi-attention and super resolution, claiming and supporting their fitness for this domain. We evaluate our approach on three fake satellite imagery datasets based on different generative models and sizes, obtaining 99.46%, 92.81%, and 99.50% accuracies. We perform comparisons to SOTA deepfake detectors where our detector overperforms the second best one by 15%. We also analyze attention maps for interpretibility of our detector, and conduct ablation studies to support our architectural choices.