Tootfinder

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection
Ji Qi, Xinchang Zhang, Dingqi Ye, Yongjia Ruan, Xin Guo, Shaowen Wang, Haifeng Li
https://arxiv.org/abs/2506.20599

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection
The rapid advancement of generative artificial intelligence is producing fake remote sensing imagery (RSI) that is increasingly difficult to detect, potentially leading to erroneous intelligence, fake news, and even conspiracy theories. Existing forgery detection methods typically rely on single visual features to capture predefined artifacts, such as spatial-domain cues to detect forged objects like roads or buildings in RSI, or frequency-domain features to identify artifacts from up-sampling …

CAM-NET: An AI Model for Whole Atmosphere with Thermosphere and Ionosphere Extension
Jiahui Hu, Wenjun Dong
https://arxiv.org/abs/2506.19340 https://

CAM-NET: An AI Model for Whole Atmosphere with Thermosphere and Ionosphere Extension
We present Compressible Atmospheric Model-Network (CAM-NET), an AI model designed to predict neutral atmospheric variables from the Earth's surface to the ionosphere with high accuracy and computational efficiency. Accurate modeling of the entire atmosphere is critical for understanding the upward propagation of gravity waves, which influence upper-atmospheric dynamics and coupling across atmospheric layers. CAM-NET leverages the Spherical Fourier Neural Operator (SFNO) to capture global-scale …