Image forgery detection: a survey of recent deep-learning approaches

In the last years, due to the availability and easy of use of image editing tools, a large amount of fake and altered images have been produced and spread through the media and the Web. A lot of different approaches have been proposed in order to assess the authenticity of an image and in some cases to localize the altered (forged) areas. In this paper, we conduct a survey of some of the most recent image forgery detection methods that are specifically designed upon Deep Learning (DL) techniques, focusing on commonly found copy-move and splicing attacks. DeepFake generated content is also addressed insofar as its application is aimed at images, achieving the same effect as splicing. This survey is especially timely because deep learning powered techniques appear to be the most relevant right now, since they give the best overall performances on the available benchmark datasets. We discuss the key-aspects of these methods, while also describing the datasets on which they are trained and validated. We also discuss and compare (where possible) their performance. Building upon this analysis, we conclude by addressing possible future research trends and directions, in both deep learning architectural and evaluation approaches, and dataset building for easy methods comparison.

Automated summary

In recent years, there has been a proliferation of fake and altered images due to the availability and ease of use of image editing tools. This paper conducts a survey of the latest image forgery detection methods based on Deep Learning (DL) techniques, focusing on copy-move and splicing attacks. The survey discusses the key aspects of these methods, the datasets used for training and validation, as well as their performance. The paper also addresses future research trends and directions in deep learning architectures and evaluation approaches, as well as dataset building for easy methods comparison.