# WMNet: A Lossless Watermarking Technique Using Deep Learning for Medical Image Authentication  
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Footnote 1: email: [EMAIL_ADDRESS]. picture archiving and communication systems to digitalize the storage and transmission of medical images. However, it is necessary to address the privacy and security of these medical images. [PERSON] et al. [8] proposed a discrete Fourier transform-based watermarking mechanism that allows effective management of medical images. This method encrypts the electronic patient record (EPR) with a secret key and embeds it into the medical image as a watermark. Not only can this method verify the copyright of the protected image, but it can also manage the image based on the embedded EPR data. [PERSON] et al. [9] hid the EPR data of patients in medical images to ensure the security of the data in the electronic healthcare system. [PERSON] et al. [10] proposed a multiple-watermark method for medical images based on discrete wavelet transform (DWT). This method is designed to place the highest priority on the safety of the EPR data. However, most medical watermarking systems embed watermarks that represent medical image ownership into medical images through algorithms. This causes the quality of the watermark-embedded image and the original image to differ, and as medical images contain many fine features, traditional watermarking methods may tamper with crucial data inside the images. Even small changes may distort fine characteristics and affect the decision-making of medical staff, resulting in unnecessary disputes. For example, a watermark-embedded image produces an inexplicable afterimage because of the embedding of watermark information. Medical staff cannot identify this afterimage as a symptom of the distortion caused by watermark embedding. This leads to confusion among the medical staff and influences their judgments. Therefore, some researchers have focused on developing lossless medical watermarking techniques [14; 15; 16; 17]. The key to lossless medical watermarking techniques is the extraction of appropriate features from images. These image features and watermarks are used to generate a verification image that can be used to validate the image. This image is referred to as the ownership share image (OSI). For image verification, image features are extracted in the same way and this feature can be created the master share image (MSI). Finally, the previous OSI and MSI can be used to recover the watermark. Thus, the image is not destroyed. In addition, a watermarking system generally involves a watermark embedding procedure and a watermark extraction procedure. During watermark embedding, a watermark representing image ownership is embedded by an algorithm into the image to be protected. During watermark extraction, the algorithm is used to extract the watermark from suspected images to authenticate those images. However, conventional measurement systems for watermarks use human vision to identify the information contained within the extracted watermark or use similarity estimation methods, such as bit error rate (BER) or normalized correlation (NC), to confirm that the system can retrieve the watermark. This process is not completely objective. For example, if an extracted watermark has already been distorted and the original content cannot be discerned from the overall appearance, the watermark will still be considered valid if the calculated BER or NC is high. In this paper, a deep learning model for detecting distortion watermarks, called WMNet, is proposed. WMNet is designed based on the convolutional neural network (CNN) architecture [25]. In recent years, the most popular network architecture for deep learning for images is the CNN and shows very good performance in the application of deep learning [26; 27].