Zero-Watermarking for Medical Images Based on Regions of Interest Detection using K-Means Clustering and Discrete Fourier Transform

Watermarking schemes ensure digital image security and copyright protection to prevent unauthorized distribution. Zero-watermarking methods do not modify the image. This characteristic is a requirement in some tasks that need image integrity, such as medical images. Zero-watermarking methods obtain specific features for the master share construction to protect the digital image. This paper proposed a zero-watermarking scheme based on K-means clustering for ROI detection to obtain specific features. The K-means algorithm classifies the data according to the proximity of the generated clusters. K-means clustering is applied for image segmentation to identify ROI and detect areas that contain important information from the image. Therefore, the Discrete Fourier Transform (DFT) is applied to the ROI features, using the high frequencies to increase its robustness against geometric attacks. In addition, an edge detection based on the Sobel operator is applied for the QR code creation. This type of watermark avoids errors in watermark detection and increases the robustness of the watermark system. The master share creation is based on an XOR logic operation between extracted features from the selected ROI and the watermark. This method focuses on the protection of the image despite it being tampered with. Many proposed schemes focus on protection against advanced image processing attacks. The experiments demonstrate that the presented algorithm is robust against geometric and advanced signal-processing attacks. The DFT coefficients from the extracted ROI features increase the efficiency and robustness.

@article{Arevalo-Ancona2023,
  title     = {Zero-Watermarking for Medical Images Based on Regions of Interest Detection using K-Means Clustering and Discrete Fourier Transform},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {6},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Rodrigo Eduardo Arevalo-Ancona and Manuel Cedillo-Hernandez},
  doi       = {10.14569/IJACSA.2023.0140662},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140662}
}