# Review of Image Forensic Techniques Based on Deep Learning
## 3 Image Forgery Detection Based on Deep Learning
### Image Copy-Move Forgery Detection  
The CMFD methods detect tampered images by extracting features associated with tampering. For an image, it is possible to globally capture the features either for the entire image or locally for regions. The choice of feature extraction methods affects the performance of CMFD methods greatly. The CMFD methods are divided into two categories, conventional manual feature-based methods and deep learning-based methods. The conventional manual feature methods can be divided into block-based and keypoint-based CMFD methods. The block-based CMFD methods can locate tampered  
Figure 4: A flowchart of JPEG compression. Figure 5: An overview of image forgery detection based on deep learning. regions accurately, but there are main problems, such as high computational complexity and difficulty in resisting large-scale rotation and scaling. To solve these problems, keypoint-based CMFD methods are proposed. The keypoint-based CMFD methods use the keypoint extraction techniques to extract keypoints of the images and find similar features using feature-matching algorithms. The keypoint-based CMFD methods can accurately locate tampered regions of common tampered images, but it mainly suffers from problems, such as the small number of keypoints in smooth regions leading to undetectable and poor algorithm generalization ability. To solve the problems of conventional manual feature methods, deep learning-based CMFD methods are proposed. With the rapid development of deep learning techniques, deep learning-based methods have been applied to the field of tampering detection. Deep learning-based CMFD methods have shown great performance improvements. A well-trained model can learn the latent features of images. The difference between the two types of images is found to discriminate the tampered image. Compared with conventional methods, deep learning-based CMFD can provide more accurate and comprehensive feature descriptors. To detect whether an image was tampered, [PERSON] and [PERSON] [38] proposed a deep convolutional neural network (DCNN)-based model that used the spatial rich models (SRM) with 30 basic high-pass filters to initialize the weights of the first layer, which helped suppress the effects of image semantics and accelerated the network convergence. The image features were extracted by the convolutional layer and classified using a support vector machine (SVM) to discern whether the image was tampered with or not. [PERSON] and [PERSON] [39] proposed a convolutional neural network (CNN)-based model to automatically extract image features for image tampering classification with robust to image compression, scaling, rotation, and blurring, but the method also required analysis at the pixel level to locate tampered with regions. In [38, 39], the authors only perform the detection of whether tampering has been performed, and cannot localize tampered regions, which has a limited application. To further improve the application space of the algorithm and achieve the localization of copy and move regions, [PERSON] et al. [40] proposed a method combining image segmentation and DCNN, using Super-BPD [41] to segment the image to obtain the edge information of the image. VGG 16 and atrous spatial pyramid pooling (ASPP) [42] networks obtained the multi-scale features of the image to improve the accuracy of the algorithm. The feature matching module was introduced to achieve the localization of tampered regions. But the segmentation module leads to high computational complexity. [PERSON] et al. [43] designed a two-stage detection framework. The first stage introduced atrous convolution with autocorrelation matching based on spatial attention to improve similarity detection. In the second stage, the superglue method was proposed to eliminate false warning regions and repair incomplete regions, thus improving the detection accuracy of the algorithm.