Hybrid watermarking algorithm for medical images based on digital transformation and MobileNetV2

Abstract

The current study presents a new methodology for zero watermarking that relies on the discrete wavelet transform (DWT) and an improved MobileNetV2 convolutional neural network, along with the discrete cosine transform (DCT). This model is intended to overcome the issue of algorithm robustness in encrypted medical image watermarking. The proposed technique targets medical images inside the area of encryption and offers a unique watermarking approach to overcome the issues above. The coefficients acquired from the fully linked network layer are converted using DWT and DCT to create the medical image's feature vector. Second, MobileNetV2 is initially fed the medical image; this network has been prepared by adjusting parameters such as the convolution kernels' size and the convolution modules' typical architecture. Finally, the encryption of watermarks is achieved through the utilization of the logistic map system and hash function, whereby an independent party securely stores the requisite keys. The integration of a zero-watermark involves the execution of logical operations on both the encrypted watermarks and the attributes of the source image. The results of the experiment indicate that the algorithm can effectively differentiate encrypted medical images and recover the initial data from encrypted watermarked material despite conventional and geometric attacks. Compared to alternative algorithms, their superior resilience and invisibility are noteworthy.