# Review on Semi-Fragile Watermarking Algorithms for Content Authentication of Digital Images
## 5 Overview on Semi-fragile Watermarking Algorithms
### Recoverable Semi-Fragile Watermarking Algorithms
#### 5.2.3 Other Transform Domains  
In view of the high fidelity requirements for watermarked images, the method of integer wavelet transform (IWT) occurs, and the integer sequence is mapped to integer wavelet coefficients. [PERSON] et al. [PERSON] et al. (2018) put forward a semi-fragile watermarking algorithm in IWT domain. This algorithm proposes a novel semi-fragile data-hiding technique based on IWT and quantization index modulation (QIM), which is used for tamper detection and correction. Dither modulation (DM) is a kind of special case of QIM. In this paper, the half-toning technology is used to get a watermarking image digest to strengthen the ability of tamper recovery. Since both of the process of embedding and the relatively reliable binary data modulation need to balance the imperceptibility and robustness, the DM-QIM is used to replace the spread-spectrum technique. If the percentage of the tampered areas in the watermarked image is less than 40%, the algorithm can recover the tampered areas effectively. However, the algorithm cannot resist attacks such as collage and VQ attacks. As the embedded space is fixed, the quantity of signal points of the host image used to embed the watermark will be reduced due to the increase of the payload, which may lead to poor detection performance. The pinned sine transform (PST) can decompose the image into boundary regions and other regions. Since texture features can be reflected well by boundary regions and most legal image processing operations will not change the features, the watermarking data is embedded in the boundary areas to identify and locate the tampered areas. [PERSON] et al. [34] put forward a novel semi-fragile watermarking algorithm using pinned sine transform. The watermark containing the texture features of the original image is embedded into the pinned region of PST, which makes the algorithm extremely sensitive to any changes of texture information in the watermarked image. The key point of this algorithm is to protect the primary texture information of the image, like image edge. If such texture information is destroyed, the watermark cannot survive in the authentication process. As for this authentication algorithm, its probability of tamper detection is higher than 98%, so it can achieve the localization of the tampered areas accurately and recover them approximately. In addition, it has lower sensitivity for acceptable image processing operations than that of the equal algorithms in DCT domain. However, the performance of the algorithm for other non-malicious tampering operations is not involved. Due to the property that the symbols of the majority of Slant intermediate frequency coefficients can remain unchanged before and after non-malicious tampering, [PERSON] et al. [9] raised a semi-fragile watermarking algorithm in Slant transform domain. The process of watermark embedding is completed by adjusting the values of coefficients in Slant domain, and the process of recovery for the tampered areas is achieved by the LSB algorithm. The algorithm can tolerate the JPEG compression with QF larger than 75% and Gaussian noise with variance less than 0.005. It can also effectively resist the simultaneous implementation of some malicious tampering and non-malicious tampering. However, the algorithm uses a separate block technology, which gives it problems surrounding security and detection accuracy. In addition, the LSB-based recovery algorithm cannot resist the JPEG compression effectively. To make the algorithm achieve better performance, two different transform domain methods can be used together. [PERSON] and [PERSON] [35] proposed a smart watermarking image authentication and recovery algorithm by combining the DCT domain method and the IWT domain method together. The HVS based on genetic programming (GP) is used in this algorithm. The watermarking information is generated and embedded in the image by using DCT and IWT domain methods.