# Deep Learning-Based Digital Image Forgery Detection System
## 2 Literature Review  
Recent developments of image forensic techniques have led to the emergence of state-of-the-art techniques with which we can detect manipulations that have been made in digital images. Previously, some research studies [10; 11; 12] have proposed approaches that rely on the observations that are made during each phase of the image history, from its acquisition phase to saving it in a compressed format. The processing of the image leaves a trace on the image for the verification of digital authenticity. It is then determined as authentic or inauthentic by the verification of a digital signature. [PERSON] et al. [2] suggested a new approach for the detection of image forgery. This technique is not adding digital watermarking in the images and does not compare the images for training and testing. The authors proposed that image features extracted during the acquisition phase are themselves proof of authenticity of the image. These features are often visible to the naked eye. Specifically, it uses image artifacts caused by various irregularities as markers to determine image validity. [PERSON] et al. [3] proposed a technique for detecting image tampering using a color filter array. It computes a single feature and a simple threshold-based classifier. The authors tested their approach with authentic, computer-generated, and tampered images. The experimental analysis showed low error rates. [PERSON] et al. [13] performed a research survey that was based on deep learning techniques for the task of image forgery detection, and they presented an analysis of the approaches used to detect the authenticity of images on publicly available datasets. [PERSON] et al. [14] introduced a deep learning-based architecture for copy/move image forgery detection using BusterNet, which is an end-to-end trainable approach. BusterNet uses two-branch architecture. The goal of the first branch is to identify manipulation areas using visual artifacts, whereas the second branch identifies copy/move areas using visual similarities. For effective BusterNet training, they proposed simple techniques for out-of-domain datasets and a stepwise approach. Their extensive research study demonstrated that BusterNet outperformed traditional copy/move algorithms by a large margin. The proposed architecture was evaluated with the CASIA and CoMoFoD datasets. [PERSON] et al. [15] discussed the importance of detecting tampering in images using deep learning-based techniques on publicly available datasets such as CASIA, UCID, MICC [9; 16; 17], and so forth. They covered passive image forensic analysis methodology and highlighted future challenges in developing a mechanism for the detection of tampered images. In another study, [PERSON] et al. [18] proposed a unique IDF technique based on a CNN. The goal of this technique is to automatically learn how image modification could be done. The proposed IDF technique takes image-altering features as input generated after destroying the contents of an image. Since tampering alters some resident associations, this technique focused on examining the local operational association among pixels rather than focusing on the look and feel of the image; it then detects forgery in an image. In another study, [PERSON] et al. [19] proposed a CNN-based architecture for the detection of digital image forgery. They proposed that the first layer of the CNN model is directly involved in the preprocessing stage. It searches for the issues that occur after tampering. They trained the CNN model on trial images, whereas SVM was used for the detection of manipulations. [PERSON] et al. [20] proposed a ringed residual U-Net (RRU-Net) for forgery detection in image slicing. They proposed an architecture where forgery detection is employed using an end-to-end image segmentation network. The goal of the RRU-Net study was to use human brain mechanisms to develop an approach using RRU-Nets, which can detect manipulations without pre- and post-processing. Generally, the human brain works on recall and consolidation mechanisms.