## 2 Related Work
This facilitates embedding two watermarks: the highly robust \"patient report\" and the less robust \"patient medical image\" directly into the singular values of the chosen DWT sub-bands. The study of [PERSON] and [PERSON] [17] proposed a novel image watermarking in which the HL sub-band of DWT is subjected to Homomorphic Transform (HT) separating the illumination and reflectance components. The watermark is then embedded into the singular values of the reflectance component using SVD. Inspired by the latter, [PERSON] et al. [18] employed a pretrained Deep Neural Network (DNN) to extract key features of the image to be watermarked. The DWT and Discrete Cosine Transform (DCT) are utilized to transform these features into a more compact representation for more robustness. To further enhance the security, the watermark undergoes chaotic scrambling before being XORed with the medical image using a secret key. [PERSON] et al. [19] developed a method that embeds feature information in the Region of Interest (ROI) using the DWTand tag information in the Non-Interest Region (NIR) using the DCT. The process starts by segmenting the original medical image into the ROI, containing crucial details, and an NIR. A two-level DWT is then applied to the ROI, generating specific coefficients (LL1, LH1, HL1, HH1, LL2, LH2, HL2, HH2). The watermark for quality features is embedded by modifying the low-frequency coefficients (LL2) of the DWT decomposition. Next, the NIR is divided into non-overlapping 8 \(\times\) 8 blocks, and each block undergoes a DCT. The DCT coefficients are then rearranged using a zigzag scan, and eight intermediate-frequency coefficients are chosen for embedding the private label watermark. Another work of [PERSON] and [PERSON] [20] explored two watermarking DWT-based schemes for medical image watermarking. The first scheme employed a single-level SVD on each DWT sub-band, while the second used a two-level SVD to compare the impact of SVD levels on the robustness and imperceptibility of the watermarking. Among the modern methods of watermarking images is the use of gradients. The method proposed by [PERSON] et al. [21] addresses medical image watermarking challenges by proposing a zero-watermarking scheme that combines feature extraction and encryption. The method extracted a feature vector using the Histogram of Oriented Gradients (HOG), capturing image's edge information, which is transformed into a compact feature sequence through a perceptual hashing algorithm. The watermark image undergoes chaotic scrambling via a logical mapping process and then is embedded into the medical image. In the approach of [PERSON] et al. [22], the information is embedded in the LSB of the image by first dividing the image into non-overlapping 16 \(\times\) 16 blocks, then for each block, the gradient magnitude and direction are calculated, and the central pixel's LSB is selected for embedding. After scrambling with a chaotic system for security, the watermark bits are embedded based on the gradient values (i.e., stronger magnitudes or directions allow for more watermark bits to be embedded). The final watermarked image is constructed by combining the modified LSB with the original most significant bits (MSBs). In the method proposed by [PERSON] et al. [23], the cover image is decomposed with a specific DWT followed by a targeted transformation, using the DCT, on certain parts. Then, the smoothness of different image regions is analyzed to guide the division and embedding of the watermark, with specific thresholds determining the placement. The efficiency of this method hinges on using image features (smoothness) to select embedding locations. This approach enhances security by requiring a secret key based on the chosen embedding locations for successful watermark extraction. A key challenge in medical image watermarking lies in achieving a balance between the desired properties of imperceptibility, capacity, and robustness.