Robust and Secure Zero-Watermarking Algorithm for Medical Images Based on Harris-SURF-DCT and Chaotic Map

Abstract

To protect the patient information in medical images, this article proposes a robust watermarking algorithm for medical images based on Harris-SURF-DCT. First, the corners of the medical image are extracted using the Harris corner detection algorithm, and then, the previously extracted corners are described using the method of describing feature points in the SURF algorithm to generate the feature descriptor matrix. Then, the feature descriptor matrix is processed through the perceptual hash algorithm to obtain the feature vector of the medical image, which is a binary feature vector with a size of 32 bits. Secondly, to enhance the security of the watermark information, the logistic map algorithm is used to encrypt the watermark before embedding the watermark. Finally, with the help of cryptography knowledge, third party, and zero-watermarking technology, the algorithm can embed the watermark without modifying the medical image. When extracting the watermark, the algorithm can extract the watermark from the test image without the original image. In addition, the algorithm has strong robustness to conventional attacks and geometric attacks. Especially under geometric attacks, the algorithm performs better.

Robust Watermarking Algorithm against the Geometric Attacks based on Non-Subsampled Shearlet Transform and Harris-Laplace Detector

“…e redundancy of analysis is very large [12,13,18] Based on curvelets It is so efficient for natural images whose edge lines are dominated by curves Curvelets have high redundancy [14,19] Based on contourlets Contourlets inherits the benefits from the ridgelet transform and the curvelet transform Contourlets have no translation invariance [15,16,[20][21][22][23] Based on shearlet Shearlet possesses the characteristics of multi-scale and the ability to capture the geometry of multidimensional data e discrete shearlet transform may produce the pseudo-Gibbs phenomenon [24,25] Based on PHFMs PHFMs are image rotation invariant It would lead to higher numerical errors when processing smaller images [26,27] Based on Harris detection Harris feature points have good rotation invariance Harris feature points are short of the scale invariance and affine invariance [28] Based on Harris-Laplace detection…”

“…To further verify the performance of the proposed watermarking algorithm, it is compared with previous typical schemes [23,27,30]. Since the singular values of the BSVD are small variation when image is faced with a little distortion, and the DST possesses the multidirectional properties, the scheme of combining the DST with the BSVD is applicable to images with different textures [23].…”

“…However, the exhaustive method of the ASIFT involves heavy computations, and the physical significance of affine transform on the original image is not obvious. Afterwards, a robust watermarking algorithm for medical images based on the Harris-SURF-DCT was proposed to protect the patient information [27]. e Harris detector was utilized to extract the feature points of the medical image, and the extracted points were described through the speeded up robust features (SURF) algorithm to generate the feature descriptor matrix.…”

“…Hence, these watermarking algorithms based on image feature points with better robustness to resist the geometric attacks were discovered, such as Harris detection [26,27], Harris-Laplace detection [28], scale-invariant feature transformation (SIFT) [29], and affine scale-invariant feature transformation (ASIFT) [30]. During the detection process, the watermark is less prone to synchronization error.…”

“…e ASIFT is applied to obtain feature points invariant to geometric attacks. Gong et al presented a robust watermarking algorithm for medical images based on the Harris-SURF-DCT [27].…”