# Review of Image Forensic Techniques Based on Deep Learning
## 1 Introduction
In this survey, we focus on passive image forgery detection technology based on deep learning and robust image watermarking technology based on deep learning. There have been many reviews of passive forgery detection techniques over the last few years. [PERSON] et al. [2] created a detailed overview of the process of image tampering and the current problems of tampering detection, and sorted out the conventional algorithms in tampering detection from different aspects, without reviewing the techniques of deep learning. [PERSON] et al. [3] sorted out and compared the conventional copy-move tampering detection algorithms, giving the advantages and disadvantages of each conventional algorithm in detail, without reviewing the techniques related to deep learning. [PERSON] et al. [4] reviewed deep learning-based tampering algorithms and compared copy-move, splicing,  
Figure 1: Classification of image forensic techniques. and deep fake techniques. However, generic tampering detection algorithms were not provided in detail. [PERSON] et al. [5] reviewed image and video tampering detection algorithms, giving a detailed comparison of tampering detection datasets and algorithms, but did not summarize the evaluation metrics of tampering detection algorithms. [PERSON] et al. [6] focused on the universal type-independent techniques for image tampering detection. Some generic methods based on resampling, inconsistency-based detection, and compression are compared and analyzed in terms of three aspects: the methodology used, the dataset and classifier used, and performance. A generic framework for image tampering detection is given, including dataset selection, data preparation, feature selection and extraction, classifier selection, and performance evaluation. The top journals and tampering public datasets in the field of tampering detection are organized. Finally, a reinforcement learning-based model is proposed to provide an outlook on future works. However, in-depth analysis and summary of image tampering detection-based deep learning are not presented. This survey provides an overview of deep learning techniques and reviews the latest generic tamper detection algorithms. In this survey the evaluation metrics commonly used for tampering detection are summarized. For active forensics, [PERSON] et al. [7] analyzed a fragile watermarking model for tampering detection. [PERSON] et al. [8] summarized the existing work on watermarking from the perspective of blind and non-blind watermarking, robust and fragile watermarking, but it did not focus on the methods to improve the robustness. [PERSON] et al. [9] summarized the reversible watermarking model and analyzed its robustness. [PERSON] et al. [10] reviewed the robustness and imperceptibility of the watermarking model from the spatial domain and transform domain perspectives. [PERSON] and [PERSON] [11] analyzed the watermarking models based on deep watermarking in recent years, but they did not discuss the methods to improve the robustness of the models for different attacks. [PERSON] et al. [12] analyzed robustness enhancement methods for geometric attacks in deep rendering images, motion images, and screen content images. [PERSON] and [PERSON] [13] analyzed the characteristics of removal, geometric and statistical attacks and summarized the corresponding attack robustness enhancement methods, but there was less analysis of watermarking models based on deep learning. Compared to the existing active forensic reviews, we start from one of the fundamentals of model robustness enhancement (i.e., generating attack-specific adversarial samples). According to their compatibility with deep learning-based end-to-end watermarking models, the attacks are initially classified into differentiable attacks and non-differentiable attacks. According to their impact on the watermarking model, the network structure and training methods to improve the robustness of different attacks are further subdivided.