A perceptive watermarking approach applied to COVID-19 imaging data
Sandra L. Gomez-Coronel, Ernesto Moya-Albor, Jorge Brieva, Hiram Ponce-Espinosa · 2020
This work presents a watermarking algorithm applied to medical images of COVID-19 patients, intending to preserve its diagnose and that it not be modified when watermark will be inserted. Besides, we tried to protect the information of the patient using an imperceptible watermarking. Our technique is based on a perceptive approach to insert the watermark by decomposing the medical image using the Hermite transform. We use as watermark two image logos, including text strings to demonstrate that the watermark can contain relevant information of the patient. Some metrics were applied to evaluate the performance of the algorithm. Finally, we present some results about robustness with some attacks applied to watermark images.