You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A recent survey of self-embedding fragile watermarking scheme for image authentication with recovery capability 
Chapter Title:  Embedding method: transform domain 
1. Abstract of Cited Paper (BIB001):  Due to the wide variety of attacks and the difficulties of developing an accurate statistical model of host features, the structure of the watermark detector is derived by considering a simplified channel model. In this paper, we present a fragile watermarking based on block-mapping mechanism which can perfectly recover the host image from its tampered version by generating a reference data. By investigating characteristics of watermark detector, we make an effective analysis such as fragility against robustness measure and distinguish its property. In particular, we derive a watermark detector structure with simplified channel model which focuses on the error probability versus watermark-to-noise-ratio curve and describes a design by calculating the performance of technique, where attacks are either absent or as noise addition. 
2. Abstract of Cited Paper (BIB002):  The ever-growing numbers of medical digital images and the need to share them among specialists and hospitals for better and more accurate diagnosis require that patients’ privacy be protected. As a result of this, there is a need for medical image watermarking (MIW). However, MIW needs to be performed with special care for two reasons. Firstly, the watermarking procedure cannot compromise the quality of the image. Secondly, confidential patient information embedded within the image should be flawlessly retrievable without risk of error after image decompressing. Despite extensive research undertaken in this area, there is still no method available to fulfill all the requirements of MIW. This paper aims to provide a useful survey on watermarking and offer a clear perspective for interested researchers by analyzing the strengths and weaknesses of different existing methods. 
3. Abstract of Cited Paper (BIB003):  Feature extraction is a critical element in the design of image self-recovery and watermarking algorithms and its quality can have a big influence on the performance of these processes. The objective of the work presented in this thesis is to develop an effective methodology for feature extraction in the discrete cosine transform (DCT) domain and apply it in the design of adaptive image self-recovery and image watermarking algorithms. ::: The methodology is to use the most significant DCT coefficients that can be at any frequency range to detect and to classify gray level patterns. In this way, gray level variations with a wider range of spatial frequencies can be looked into without increasing computational complexity and the methodology is able to distinguish gray level patterns rather than the orientations of simple edges only as in many existing DCT-based methods. ::: The proposed image self-recovery algorithm uses the developed feature extraction methodology to detect and classify blocks that contain significant gray level variations. According to the profile of each block, the critical frequency components representing the specific gray level pattern of the block are chosen for encoding. The code lengths are made variable depending on the importance of these components in defining the block’s features, which makes the encoding of critical frequency components more precise, while keeping the total length of the reference code short. The proposed image self-recovery algorithm has resulted in remarkably shorter reference codes that are only 1/5 to 3/5 of those produced by existing methods, and consequently a superior visual quality in the embedded images. As the shorter codes contain the critical image information, the proposed algorithm has also achieved above average reconstruction quality for various tampering rates. ::: The proposed image watermarking algorithm is computationally simple and designed for the blind extraction of the watermark. The principle of the algorithm is to embed the watermark in the locations where image data alterations are the least visible. To this end, the properties of the HVS are used to identify the gray level image features of such locations. The characteristics of the frequency components representing these features are identifying by applying the DCT-based feature extraction methodology developed in this thesis. The strength with which the watermark is embedded is made adaptive to the local gray level characteristics. Simulation results have shown that the proposed watermarking algorithm results in significantly higher visual quality in the watermarked images than that of the reported methods with a difference in PSNR of about 2.7 dB, while the embedded watermark is highly robustness against JPEG compression even at low quality factors and to some other common image processes. ::: The good performance of the proposed image self-recovery and watermarking algorithms is an indication of the effectiveness of the developed feature extraction methodology. This methodology can be applied in a wide range of applications and it is suitable for any process where the DCT data is available.

Literature Review:
Unlike spatial domain watermarking [6-9, 12, 14] , the popular image compression standards are compatible with frequency domains, image transformations including DCT, discrete wavelet transform (DWT) and a combination of both DCT-DWT. In many of these schemes, the watermark is taken from the conversion process to the frequency domain. Then this frequency is inserted in the original image. The inverted Fourier transformation is then applied in the second phase to form a ready-to-send watermarked image to the receiving point. Another possible explanation is that the domain transformation method provides the possibility of insertion of more information and stronger resistance to many common attacks; a prominent weakness is the higher computational cost of spatial-domain watermarking techniques. The self-embedding fragile watermarking on the spatial domain has a weakness when the attack was in the form of JPEG compression. Therefore, DCT domain was used by paper BIB001 because of its lower computational complexity and used in JPEG compression algorithm. The DCT is one of the most popular frequency transformations in image and video processing due to its simplicity and high energy compaction BIB004 . For an image block x of size N x N, the type II 2-D DCT is defined as Eq. (12). The 2-D IDCT can be expressed as Each DCT coefficient represents a certain spatial frequency. Those patterns are sometimes referred to as basis functions. Figure 10 shows the spatial frequency patterns of the 8 × 8 DCT which is the most common block size used in image processing and the size we will be using throughout this thesis. In the DCT domain, an image block is represented as a combination of these basis functions with different magnitudes and/or signs. To put it in different words, if the image blocks are thought of as visual words, then the basic functions of the DCT are the visual alphabet that composes these words. The first DCT coefficient X(0, 0) is the DC coefficient. The DC coefficient has zero frequency in both the vertical and horizontal directions and it indicates the brightness of the image block since it corresponds to the average of the pixel values in the block. The remaining coefficients are called the DC coefficients. The AC coefficients closer to the DC coefficient have lower spatial frequencies and the frequencies increase as we move away from the DC coefficient in all directions. AC coefficients respond to grey level changes that are in the same direction as their spatial frequencies; for example, the coefficients in the first column respond to grey level changes in the vertical direction (horizontal edges) since their spatial frequencies are vertical. The use of wavelet transform will mainly address the capacity and robustness of the Information-Hiding system features. The Haar wavelet transform is the simplest of all wavelet transforms. In this, the low-frequency wavelet coefficients are generated by averaging the two-pixel values and high-frequency coefficients that are generated by taking half of the difference of the same two pixels. The four bands obtained are LL, LH, HL and HH which are shown in Fig. 11 . The wavelet transform decomposes the image into four sub-bands of different frequencies, namely approximation image (LL k ), horizontal (HL k ), vertical (LH k ) and diagonal (HH k ) details where k denotes the decomposition level. In watermarking applications, lower decomposition levels are more vulnerable to image alteration as they have a lower proportion of energy as compared to higher decomposition levels. This energy is defined as in Eq. BIB002 . Where k is the decomposition level, I k denotes coefficients of the corresponding sub-band and N k and M k are sub-band dimensions. By comparing the energy of the sub-bands in the same level, i.e. as shown in Fig. 11 (LL 1 , HL 1 , LH 1 and HH 1 ), it can be seen that the energy accumulation in the horizontal detail (HL k ) is significantly more than those of the vertical and diagonal details, hence suggesting that this sub-band is more robust to image modification. In other words, even though the approximation image (LL k ) has the highest portion of the energy of the original image, embedding the watermark in this part will degrade the image quality. Therefore, the horizontal sub-band in each level can be chosen as the best candidate area for embedding the watermark, in order to achieve image quality preservation and offer robustness simultaneously. In the reviews cited by BIB003 , DWT-based methods can show more accurate visualisation results when compared to the DCT method. It is also mentioned that if we can use areas that are less sensitive to the human visual system, it will provide more opportunities to embed watermarks without degrading the quality of terracotta images and this method is the most powerful against noise. In addition, compared to DCT-based transformations, wavelet produces fewer visual artefacts because this technique does not require that images be decomposed into blocks. However, there are weaknesses in this method: (1) oscillation: one of the complexities of wavelet-based processing is the oscillation of wavelet coefficients around the singularity. This is because wavelet is a band-pass function. (2) Shift variance: another factor, which complicates the processing of wavelet, is that a small shift in the signal greatly disturbs the oscillation pattern of the wavelet coefficients around the singularity. (3) aliasing: whenever wavelet and scale coefficients change, inverted DWT cannot cancel aliasing and this causes artefacts in the reconstructed signal. (4) Lack of directionality: lack of directional selectivity greatly complicates the modelling and processing of geometric image features such as mountains and edges. This deficiency has been solved by using a dual-tree complex wavelet transform (DTCWT) BIB003 .