Optically authenticated hyperspectral imaging: detection and restoration of manipulated data

Ensuring the authenticity and integrity of hyper-spectral images is crucial for remote sensing applications, as unauthorized spectral modifications can compromise data reliability and decision-making processes. Traditional post-acquisition authentication methods leave hyperspectral data vulnerable to manipulation before applying digital protection mechanisms. We present an acquisition-stage optical authentication framework that embeds an optical key directly in the light path of a linescan hyperspectral imager before digitization. The optical key is designed from scene spectral statistics and jointly optimized for imperceptibility, manipulation detection, and post-attack spectral restoration in a data-driven manner. We initialize and optimize the optical key using distributions parameterized by per-band spectral variance from the training data, which adaptively scales the embedding strength across bands and improves robustness to variance-driven perturbations. To evaluate its resilience, we simulate an optimal attack scenario in which an adversary randomly alters 20%–80% of pixels within a target class. A detection network achieves a maximum accuracy of 97% under light perturbation and maintains above 92% even at 80% manipulation, while maintaining classification performance within 0.6-1.2% of unsigned data. The restoration network brings spectral signatures to within 0.9-1.3° SAM. Experiments on simulated and laboratory datasets show robustness gains of up to 4.7% over traditional methods, with preserved downstream performance.