# WMNet: A Lossless Watermarking Technique Using Deep Learning for Medical Image Authentication
## 2 Lossless Watermarking Scheme  
Traditionally, when designing a watermarking mechanism to be applied to multimedia, it is expected to meet the characteristics of security, invisibility, robustness, and capacity of an effective watermarking mechanism. However, this is usually a trade-off problem, and several scholars have devoted themselves to identifying the best solution. [PERSON] et al. [28] proposed a copyright protection mechanism based on DWT and successive mean quantization transform successive mean quantization transform for medical images. Although this scheme achieved satisfactory experiment results, the approaches of malicious attacks that can be resisted do not include most image processing methods. [PERSON] et al. [29] explored the security protection of electronic health records adopting different biometrics in the arena of the internet of medical things, which effectively improved the watermark information embedding of algorithms. To develop a better watermarking mechanism, scholars subsequently put forward the lossless watermarking scheme [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. The major key point in the design of a lossless watermarking scheme lies in the extraction of image features. As long as the quality of the protected image is not compromised, this scheme is ideal for protecting sensitive images, such as military and medical images. The flow chart of the generalized lossless watermarking scheme is shown in Figure 1. Image features are extracted from the host image through image capture technology (such as discrete cosine transform and DWT), and then an OSI is generated, which is submitted to a third party for registration. When the ownership of a suspected image needs to be verified, the image is extracted by the same image capture technology, and after the features are extracted, the MSI is generated. Finally, the OSI and MSI are calculated to obtain the watermark and the copyright is confirmed. Mechanisms of this type have several notable characteristics. First, the image features extracted from the image are combined with the watermark to generate the OSI and MSI, and the OSI and MSI can be combined with an encryption algorithm to improve the security of the watermarking mechanism. For example, the generated OSI and MSI can be combined with visual cryptography [30] or visual secret sharing [31]. Second, the watermark used by this type of mechanism is not limited by the size of the protected image. In conventional watermarking mechanisms, the size of the watermark used to verify the image is usually clearly defined, which limits the number of watermarks that can be embedded in the image. However, the size of the watermark is independent of the host image in a lossless watermarking scheme. For the mechanism proposed by [PERSON] et al. [14], watermarks of different sizes used in the experiment did not affect the quality of the protected images. Moreover, building on this idea, [PERSON] et al. [15] encoded the watermark  
Figure 1: Flow chart of generalized lossless watermarking scheme. using error correction codes and then generated the OSI and MSI with the image features. Although the damage resistance of the watermark is improved, the size of the resulting OSI is much larger than that of the original watermark. This requires additional processing time to validate the image, which is not suitable for real-time systems. In addition, lossless watermarking scheme performance depends on the method used to extract the image features, such as using different frequency domains. [PERSON] et al. [16] used DWT to extract low-frequency features of different resolution levels in the image and, thereby, generate the OSI and MSI for image verification. [PERSON] et al. [17] divided an image into non-overlapping blocks and conducted DCT conversion to obtain the low-frequency features of each block as the basis for generating the OSI and MSI. This concept can effectively resist common image compression attacks. [PERSON] et al.