# Deep Learning-Based Digital Image Forgery Detection System
## 1 Introduction  
Digital images have an important role in many fields such as in newspapers, digital forensics, scientific research, medicine, and so forth. Nowadays, the usage and sharing of digital images on social media platforms is also widespread. Digital images are considered one of the main sources of information. Considering the excessive use of image sharing through various social media platforms such as WhatsApp, Instagram, Telegram, and Reddit, differentiating between real and forged images is a challenging task. The availability of many image editing software applications is making it more difficult to detect the authenticity of an image day by day. There are generally two approaches that image manipulation can be categorized into, as follows:  
1. Active approach;
2. Passive approach. With the active approach, a watermark or digital signature is embedded when the image is created. While using these embeddings, whether the image has been tampered with or not is analyzed at later stages. In the passive approach, any pre-embedded information, such as a watermark embedded for the detection of image forgery, cannot be relied upon. This approach is also known as the blind approach because there is no additional information for image forgery detection. This approach is based on features that are extracted directly from the images. Furthermore, the passive approach can be categorized into two types--independent and dependent. The independent approach detects resampling and compression forgeries,while the dependent approach detects splicing and copy/move forgeries. Figure 1 shows the hierarchy of the above approaches. In the copy/move type of manipulation, a particular part of an image is selected and then copied and pasted to another part of the same image. By doing this, the correlation value of these two parts of the image will be relatively higher compared to other parts of the image. The goal of copy/move forgery detection is to correctly identify these duplicates in these images by comparison of the attributes extracted from the features using distance measures. Two approaches, as follows, are commonly used to extract patch-wise features from the images:  
Images are divided into blocks, and features are extracted from these blocks, as discussed in [1];  
Key points are identified from the image and these key point features are extracted. The features extracted in blocks or key points are compared one by one for generating matched pairs. The duplication is confirmed if matching is found among two blocks; it confirms the duplication and can be categorized as a manipulated image. The steps of the process are shown in Figure 2. Digital image splicing is a method of extracting of objects from one image and inserting those objects into another image. It is easier to detect manipulations in copy/move image forgery detection compared to image splicing because the similar contours of an object can be easily detected in the same image since they have the same sizes, transitions, and textures. Different objects are introduced in the case of image splicing, with different textures, sizes, and transition attributes, and this approach makes forgery difficult to identify [2]. Image splicing forgery detection is dependent on the clues that are left after the manipulation of images. Some common image splicing issues include inconsistency, edge discontinuity because of the camera, and geometric and lighting conditions. Capturing  
Figure 1: Forgery detection hierarchy. Figure 2: Copy/move detection. an image from different cameras results in different attributes, and clue tampering can be confirmed [3]. There can be lighting inconsistencies as well, which can arise due to different lighting conditions. A double quantization effect can also arise when saving JPEG images because of two consecutive compression operations that are performed on the tampered image [2;3].