Seeing is Not Believing: Toward Forgery Detection for Hyperspectral Image

Amid the rapid growth of low-earth orbits (LEOs) and hyperspectral imaging (HSI) techniques, this study addresses the overlooked area of security challenges, specifically hyperspectral content manipulation or inpainting. We present HyperForensics, a novel dataset based on NASA’s AVIRIS data, covering diverse scenarios across a wavelength range of 0.4 to 2.5 micrometers. To confront the challenge of detecting small manipulated regions within HSIs, we develop a detection method using a high-resolution network (HRNet) that maintains high resolution and spatial accuracy. Experiments demonstrate this approach outperforms existing methods, highlighting the critical role of spatial-spectral features in HSI forgery detection. This pioneering work in HSI forgery benchmarking and detection invites further research to enhance HSI security.