# Review of Image Forensic Techniques Based on Deep Learning
## 1 Introduction  
Digital images are important information carriers, and with the rapid development of this technology, digital images have gradually been included in all aspects of life. However, data stored or transmitted in digital form is vulnerable to external attacks, and digital images are particularly susceptible to unauthorized access and illegal tampering. As a result, the credibility and security of digital images are under serious threat. If these illegally accessed and tampered images appear in the news media, academic research, and judicial forensics, which require high originality of images, social stability and political security will be seriously threatened. To solve the above problems, digital image forensics has become a hot issue for research and is the main method used to identify whether the images are illegally acquired or tampered with. Digital image forensic technology is a novel technique to determine the authenticity, integrity, and originality of image content by analyzing the statistical characteristics of images, which is of great significance for securing cyberspace and maintaining social order. Digital image forensics technology is mainly used to detect the authenticity of digital images and realize image copyright protection, which can be divided into active forensicstechniques and passive forensic techniques according to different detection methods, as shown in Figure 1.  
The active forensic techniques are used to embed prior information, and then extract the embedded information, such as image watermarking and image signatures, and compare it with the original information to identify whether the image was illegally obtained. Image watermarking is a technique that embeds hidden information into digital images with the aim of protecting the copyright and integrity of the images. Watermarks are usually invisible or difficult to perceive and are used to identify the owner or provide authorization information for the image. They can be used to trace and prevent unauthorized copying, modification, or tampering of the image. Image watermarking techniques typically utilize image processing algorithms and encryption techniques to ensure the robustness and security of the watermark. On the other hand, image signature is a technique used to verify the integrity and authenticity of an image. Image signatures are typically based on digital signatures using encryption and hash functions to ensure that the image has not been tampered with during transmission or storage. With the rapid development of the digital information era, there has been an alarming rise in copyright infringement facilitated by image tampering techniques. Among the aforementioned active forensic techniques, image watermarking, especially robust image watermarking that can recover watermark information intact against intentional or unintentional attacks, is more effective compared to image signatures in authenticating and protecting copyright information when images are subjected to malicious tampering. Therefore, our focus in active forensics is on image watermarking, which aligns with our research interests as well. Passive forensic techniques, which do not require prior information about the image, determine whether the image has been illegally tampered with by analyzing the structure and statistical characteristics of the image. In passive forensic techniques, even if an image has been forged beyond recognition by the human eyes, the statistical characteristics of the image will change, causing various inconsistencies in the image, and these inconsistencies are used to detect and locate tampered images. With the development of deep learning technology, deep learning technology has been widely used in many fields, such as speech processing, computer vision, natural language processing, and medical applications [1]. Deep learning technology has been widely applied in the field of image forensics, which has promoted the development of image forensics technology.