# A New Method to Detect Splicing Image Forgery Using Convolutional Neural Network
### Traditional Splicing Forgery Detection Approach  
An efficient approach for image splicing detection based on Markov features in the DCT and DWT domain was suggested by [7]. Experimental results substantiate the high performance of their approach as compared with the others. An approach for image splicing based on inter-color channel information has been introduced [8]. This approach aims to detect the most appropriate chroma-like channel, but this approach is computationally complex. An improved Markov state selection approach, which matches coefficients to Markov states based on a well-performed functional model, is proposed by [PERSON] et al. [9]. This work focuses on enhancing the Markov state selection method [10; 11], and the obtained results reveal high performance compared to the previous version. However, the number of images used in the database is not sufficient to measure the best performance of the proposed approach. An enhanced version of the Run Length Run number algorithm for ISFD is introduced by [PERSON] et al. [12], and the enhanced version used the principal component analysis (PCA) and kernel PCA, which is a blind technique for ISFD based on the Markov features for edge images in spatial domain and DCT coefficients. The efficiency of the previous merge has been proved by using PCA and SVM, suggested by [PERSON] et al. [13]. An efficient technique for the spliced blurred image can localize the spliced region proposed by [PERSON] et al. [11]. This approach can be applied only to blurred images. A Markov-based approach in the DCT and contourlet transform domains introduced by [PERSON] and [PERSON] [14]. The superiority of the proposed approach is that it can be extended in terms of gray and color image splicing detection. [PERSON] et al. [15] exposed a novel approach for ISFD, using noise discrepancies in multiple scales as an indicator for ISFD. The proposed approach reveals high superiority when compared with existing state-of-the-art approaches. An efficient approach for color ISFD was suggested by [PERSON] et al. [16]. The authors used Markov features in quaternion discrete cosine transform (QDCT), then exploited SVM to make a classification for the Markov feature vector. The experimental results reveal high superiority thanks to their approach with more than 92.38% accuracy compared with other recently published methods, but accuracy should be improved on that. An efficient algorithm based on the PCA algorithm and the K-means method has been introduced by [PERSON] et al. [17]. The experimental results specified good results for ISFD when compared to the original and spliced regions. An approach for ISFD based on local binary pattern (LBP) and DCT for feature extraction; hence, SVM, has been used for detection and is proposed by [PERSON] et al. [10]. A novel method for ISFD based on a noise level function (NLF), the values of NLF reflect the relationship between noise variance and sharpness of image blocks, is introduced by [PERSON] and [PERSON] [18]. The experimental results reveal the high superiority of the proposed method, but that approach cannot detect more areas of forgery. An efficient method for ISFD based on several algorithms: roughness measure algorithm, PCA algorithm, and SVM algorithm, is suggested by [PERSON] et al. [19]. These algorithms together enhance the overall process of ISFD. An efficient algorithm based on the optimal threshold local ternary pattern has been introduced [20], and the proposed technique achieved an accuracy of up to 98.25%. [PERSON] et al. [21] proposed an approach for detecting and localizing ISF based on noise level estimation, with high accuracy revealed  
Figure 1: Represents the splicing of original images for obtaining a spliced image forgery, where (**A**,**B**) are the original images, and (**C**) is the spliced image forgery. when experiments were performed on the CUISDE dataset. Local binary pattern (LBP) has been employed for ISFD [22], the LBP is used to compute the image texture features.