Discriminative and robust zero-watermarking scheme based on completed local binary pattern for authentication and copyright identification of medical images

Authentication and copyright identification are two critical security issues for medical images. Although zerowatermarking schemes can provide durable, reliable and distortion-free protection for medical images, the existing zerowatermarking schemes for medical images still face two problems. On one hand, they rarely considered the distinguishability for medical images, which is critical because different medical images are sometimes similar to each other. On the other hand, their robustness against geometric attacks, such as cropping, rotation and flipping, is insufficient. In this study, a novel discriminative and robust zero-watermarking (DRZW) is proposed to address these two problems. In DRZW, content-based features of medical images are first extracted based on completed local binary pattern (CLBP) operator to ensure the distinguishability and robustness, especially against geometric attacks. Then, master shares and ownership shares are generated from the content-based features and watermark according to (2,2) visual cryptography. Finally, the ownership shares are stored for authentication and copyright identification. For queried medical images, their content-based features are extracted and master shares are generated. Their watermarks for authentication and copyright identification are recovered by stacking the generated master shares and stored ownership shares. 200 different medical images of 5 types are collected as the testing data and our experimental results demonstrate that DRZW ensures both the accuracy and reliability of authentication and copyright identification. When fixing the false positive rate to 1.00%, the average value of false negative rates by using DRZW is only 1.75% under 20 common attacks with different parameters

Content-adaptive reliable robust lossless data embedding

It is well known that robust lossless data embedding (RLDE) methods can be used to protect copyright of digital images when the intactness of host images is highly demanded and the unintentional attacks may be encountered in data communication. However, the existing RLDE methods cannot be applied satisfactorily to the practical scenarios due to different drawbacks, e.g., serious "salt-and-pepper" noise, low capacity and unreliable reversibility. In this paper, we propose an effective solution to RLDE by improving the histogram rotation (HR)-based embedding model. The proposed method is a content-adaptive reliable RLDE or CAR for short. It eliminates the "salt-and-pepper" noise in HR by the pixel adjustment mechanism. Therefore, reliable regions for embedding can be well constructed. Furthermore, we basically expect the watermark strengths to be adaptive to different image contents, and thus we have a chance to make an effective tradeoff between invisibility and robustness. The luminance masking together with the threshold strategy is duly adopted in the proposed RLDE method, so the just noticeable distortion thresholds of different local regions can be well utilized to control the watermark strengths. Experimental evidence on 300 test images including natural, medical and synthetic aperture radar (SAR) images demonstrates the effectiveness of the proposed data embedding method. © 2011 Elsevier B.V