# Review of Image Forensic Techniques Based on Deep Learning
## 3 Image Forgery Detection Based on Deep Learning
### Image Splicing Forgery Detection  
Image splicing forgery is also one of the most popular ways to manipulate the content of an image. The manipulation operation of splicing is copying an area from one image and pasting it to another image. The tampered images are a serious threat to the security of image information. It is crucial to develop suitable methods to detect image splicing forgery. Image splicing detection techniques are divided into manual feature-based methods and deep learning-based methods. The current manual feature-based methods can be divided into three categories: textural feature-based techniques, noise-based techniques, and other techniques. The textural feature-based techniques use the difference between the local frequency distribution in tampered images and the local frequency of the real image to detect image splicing. The commonly used textural feature descriptors are local binary pattern (LBP) [52], gray level co-occurrence matrixes (GLCM) [53], local directional pattern (LDP) [54], etc. Since the splicing images come from two different images, the noise distribution of the tampered image is changed. Therefore, the noise-based techniques detect tamper by estimating the noise of the tampered image. The detection of splicing images can also be performed by fusing multiple features. Manual feature-based methods use different descriptors to obtain specific features, and the detection effect is various for different datasets. The generalization of manual feature-based techniques is poor. The deep learning-based methods can automatically learn a large number of features, which improves the accuracy and generalization ability performance of image splicing detection. Deep learning-based methods can learn and optimize the feature representations for forgery forensics directly. This has inspired researchers to develop different techniques to detect image splicing. In recent years, the U-Net structure has been more widely used in splicing forgery detection. [PERSON] et al. [55] proposed a tamper detection and localization network based on U-Net with multi-scale control. The network used a rotated residual structure to enhance the learning ability of features. [PERSON] et al. [56] proposed a multitask model for locating splicing tampering in an image, which fused an attention mechanism,  
Figure 6: A serial network for CMFD: (**a**) the architecture of the proposed scheme and (**b**) the architecture of copy-move similarity detection network. Adapted from [49]. densely connected network, ASPP, and U-Net. The model can capture more multi-scale features while expanding the receptive field and improving the detection accuracy of image splicing tampering. [PERSON] et al. [57] created a multi-task squeeze and extraction network for splicing localization. The network consisted of a label mask stream and edge-guided stream, which used U-Net architecture. Squeeze and excitation attention modules (SEAMs) were incorporated into the multi-task network to recalibrate the fused features and enhance the feature representation. Many researchers have also used fully convolutional networks (FCN) commonly used in semantic segmentation for image splicing forgery detection. [PERSON] et al. [58] proposed a residual-based fully convolutional network for image splicing localization. The residual blocks were added to FCN to make the network easier to optimize. [PERSON] et al. [59] created a dense fully convolutional network for image tampering localization. This structure comprised dense connections and dilated convolutions to capture subtle tampering traces and obtain finer feature maps for prediction. [PERSON] et al. [60] proposed an FCN with a noise feature. The network enhanced the generalization ability by extracting noise maps in the pre-processing stage to expose the subtle changes in the tampered images and improved the robustness by adding the region proposal network. The technique could accurately locate the tampered regions of images and improve generalization ability and robustness.