Abstract

Zero-watermarking is one of the solutions for image copyright protection without tampering with images, and thus it is suitable for medical images, which commonly do not allow any distortion. Moment-based zero-watermarking is robust against both image processing and geometric attacks, but the discrimination of watermarks is often ignored by researchers, resulting in the high possibility that host images and fake host images cannot be distinguished by verifier. To this end, this paper proposes a PCET- (polar complex exponential transform-) based zero-watermarking scheme based on the stability of the relationships between moment magnitudes of the same order and stability of the relationships between moment magnitudes of the same repetition, which can handle multiple medical images simultaneously. The scheme first calculates the PCET moment magnitudes for each image in an image group. Then, the magnitudes of the same order and the magnitudes of the same repetition are compared to obtain the content-related features. All the image features are added together to obtain the features for the image group. Finally, the scheme extracts a robust feature vector with the chaos system and takes the bitwise XOR of the robust feature and a scrambled watermark to generate a zero-watermark. The scheme produces robust features with both resistance to various attacks and low similarity among different images. In addition, the one-to-many mapping between magnitudes and robust feature bits reduces the number of moments involved, which not only reduces the computation time but also further improves the robustness. The experimental results show that the proposed scheme meets the performance requirements of zero-watermarking on the robustness, discrimination, and capacity, and it outperforms the state-of-the-art methods in terms of robustness, discrimination, and computational time under the same payloads.

Highlights

  • Medical images such as those obtained via ultrasound and magnetic resonance imaging (MRI) provide substantial evidence for clinical diagnosis, medical treatment, and research

  • Denote the moment of order n with repetition l as mn,l. e flowchart of proposed robust feature generation location selection method is seen in Figure 2, and the steps are as follows: (1) Calculation of magnitudes and reference values: calculate the moment magnitudes and denote them ai,j ai,j ę ai,j

  • Experimental Settings. e test images come from 10 grayscale medical images of 256 × 256 and 345 grayscale medical images of 512 × 512 in the TCIA library. e watermarks include 4 logo images and8 randomly generated binary sequences

Read more

Summary

Introduction

Medical images such as those obtained via ultrasound and magnetic resonance imaging (MRI) provide substantial evidence for clinical diagnosis, medical treatment, and research. High-speed mobile networks such as 5G and 6G have further accelerated the application of telemedicine and medical resource sharing. Under this circumstance, researchers are concerned with security issues such as copyright protection, ownership identification, and tamper detection. As a traditional tool that establishes a security barrier for information transmitted over open networks, has application prospects in medical image research [1, 2]. Medical images display the details and lesions of tissues and organs and have extremely high requirements for quality; the traditional robust watermarking which embeds watermarks by tampering with images is not suitable. Zero-watermarking, known as lossless watermarking, does not require modifying image contents and maintains stability after attacks, becoming an effective method of medical image authentication

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call