## 1 Introduction
Two types of VC watermarking schemes exist. An example on the first kind is the research in [27], in which the VC is used to generate two meaningless shares of the watermark: One share is embedded in the cover image, and the other share is saved into the trusted authority (TA) in order to be used later to recover the original watermark. This kind of watermarking scheme suffers from a lack of robustness, and the watermark embedding process produces a low-quality cover image. On other hand, the examples of the second kind of VC-based watermarking schemes are the research works in [28; 29; 30]. In these schemes, a share is generated from the cover image--it is called a master share--and another share is called the ownership share; the latter is composed by superimposing the master share and the watermark. Furthermore, the researchers in [25] used VC to hide some bits of the watermark in the blocks of the edge image. The main flaw of this approach is the degradation in the quality of the cover image after the embedding of the watermark. Another piece of research [23] hid the watermark inside the cover image without modifying it. However, this approach suffers from the limitation that the size of watermark is restrained by the size of the cover image. In another piece of research, the authors [28] utilized discrete wavelet transform (DWT) and VC to design a watermarking scheme to secure the copyright of the image, but this approach is also restricted by the size of the image. The researchers in [30] proposed a new watermarking scheme using DWT, VC and a secret key. The authors employ the secret key to derive the feature vectors from wavelet subands, and then the secret share is constructed by superimposing the feature share and watermark. This approach can resist several known attacks, but it is insecure according to [29]. In [26], the authors developed a watermarking approach based on singular value decomposition (SVD), DWT and VC. The feature vectors are extracted by using DWT and SVD, which are then classified using clustering algorithms. The master share is composed using the output of the clustering outcomes, and the ownership share is designed using a master share and a watermark using VC. [PERSON] and [PERSON] [31] used visual cryptography and a support vector machine (SVM) classifier to develop an image watermarking scheme to protect the copyright of digital images. The VC is used to construct the master and owner shares of an image, while SVM is used to classify the image blocks into two main classes. In addition, [PERSON] et al. [32] proposed an image forgery detection approach based on enhanced SURF and template matching. They achieved good detection results on the CASIA dataset. [PERSON] el al. [33] developed an approach using an optimization algorithm and visual cryptography to protect the authenticity and integrity of digital videos by embedding robust watermarks inside them. All of the approaches mentioned above have their limitations and problems, as follows: (1) the size of the watermark is limited by the size of the cover image; (2) some approaches fail to resist some known attacks, such as noise, median filtering, sharping, scaling or/and rotation; (3) the insecure sending of side information, such as the key or key points, causing the receipt of side cause security problems; (4) decreasing or destroying the quality of the cover image as a result of embedding; and (5) some approaches cause a pixel expansion problem in the constructed shares. In addition to all of the earlier limitations, the main purpose of all of the above approaches is to protect the copyright of single or multiple images, while the objective of our proposed approach is to detect and localize forgeries in the digital images in an accurate and efficient manner. Hence, in this research article, the researchers investigate the hypothesis that we can overcome the above limitations and problems of the state-of-art approaches by developing novel watermarking approaches using visual cryptography and famous feature extraction algorithms.