# A New Method to Detect Splicing Image Forgery Using Convolutional Neural Network
### Deep Learning-Based Splicing Forgery Detection Approach  
An effective solution for ISFD based on a fully convolutional network (FCN) is presented by [PERSON] et al. [25]. The authors first introduced a single FCN (SFCN); after that, they used multi-task FSN (MFSN), and the experimental results have shown superiority in favor of SFCN and MFCN when compared with exciting splicing localization algorithms. A novel ISFD method has been introduced by [PERSON] et al. [26]. The proposed approach depends on a coarse-to-refined convolutional neural network (C2 RNet) and diluted adaptive clustering. Experimental results reveal the high superiority of the proposed approach over other existing approaches. However, the proposed detection method only focuses on one manipulated area in the image due to the limitation of the post-processing approach and cannot detect the distortion otherwise, which is an efficient blind ISFD technique. Additionally, they employed a deep learning architecture called ResNet-Conv suggested by [PERSON] et al. [27]. The suggested model has been trained and evaluated using a computer-generated image splicing dataset and found to be more efficient than other models. [PERSON] and [PERSON] have suggested a blind ISFD technique [28]. The proposed approach used a deep convolutional residual network and a fully connected classifier network. Good results were obtained when the approach was tested using the CASIA v2.0 database. An efficient ISFD based on dual-channel U-Net, that is, DCU-Net is suggested by [PERSON] et al. [29]. Experimental results reveal the robustness of the proposed approach. Multiple ISFD techniques were proposed by [PERSON] et al. [30]. The authors used Mask R-CNN with MobileNet VI as a backbone architecture. The proposed method was tested over ultra-modern datasets such as CASIA, Wild Web, MISD, and Columbia Gray. The results specified good superiority. However, his proposed model is not tested on a larger number of attacks, and there is no comparison of evaluation results with and without attacks. A lightweight architecture based on CNN for copy-move forgery detection is introduced by [PERSON] et al. [31]. The presented approach reveals superiority in terms of time and accuracy compared with other recently published methods. However, it does not have a high-efficiency rate when it comes to splicing image fraud.  
The main contribution of this work is as follows:  
* The proposed model achieved high accuracy with a small number of parameters as compared with the recently published approaches, which can be considered as power key for the proposed architecture. Moreover, the proposed model is suitable for environments that have limitations in memory space and CPUs.
* The presented CNN model has four convolutional layers, four max-pooling layers, one global average pooling layer, one fully connected layer, and 97,698 hyper-parameters shown in Table 1, so it is a lightweight CNN model.
* Three standard datasets were used that allowed us to provide accurate experiments, and these datasets are CASIA 1.0 [32], CASIA 2.0 [32], and CUISDE [33].
* Experiments were conducted on the dataset, and an analytical comparison was made between the proposed model's results and previously presented models ([PERSON] et al. [10], [PERSON] et al. [20], [PERSON] et al. [22], [PERSON] et al. [29], [PERSON] et al. [34], [PERSON] et al. [30], [PERSON] et al. [35], [PERSON] et al. [28], and [PERSON] et al. [22]). This comparison showed that the proposed model is efficient and accurate against the other investigated models.  
This study encompasses other parts as follows: the preliminaries of CNN have been discussed through Section 2. In Section 3, we discussed the proposed approach in detail. The experimental results are explained and discussed in Section 4. Finally, through Section 5, we exposed the conclusion.