## 1 Introduction  
Nowadays, with the continuous improvement of computational power and image processing/generation algorithms, creating fake or synthetic images is no longer a difficult task, even for non-experts. The content of these images does not reflect what has really happened in the real world, and malicious use of such images may cause serious problems. In order to recover our trust in the authenticity of digital images, researchers have worked for some years on image forensics (i.e., a passive image authentication approach without actively inserting information like watermarks into images), and various forensic methods have been proposed [1; 2; 3; 4]. One particular image forensic problem is the detection of computer-graphics (CG) images created by advanced graphics rendering engines [5; 6; 7]. CG images can now have a high level of visual realism (_cf._ examples in Figure 1), and more researchers are interested in the so-called _CG forensics problem_ of distinguishing between CG images (i.e., synthetic images generated by rendering software tools) and natural images (NIs, i.e., authentic images acquired by digital cameras). Recent CG forensic methods, mostly based on the deep learning paradigm, can achieve very good performance in the classification of CG images and NIs. However, to the best of our knowledge, all existing methods for CG forensics follow the conventional supervised learning framework, which commonly requires a good quantity of labeled samples for effective training, especially for the training of deep neural networks. However, in practical applications, we often encounter scenarios with scarcity of labeled data; for example, when we want to detect CG images generated by a new rendering engine for which only a very limited number of CG images created by the targeted rendering engine are available. Under such situations, an alternative and more appealing learning mechanism is the so-called _self-supervised learning_ approach [8]. This approach appears, in this case, to be a more flexible and more useful technical solution, being less demanding regarding the number of available manually labeled samples. The main idea of self-supervised learning is to effectively make use of the prevalent unlabeled data combined with a training procedure on a self-supervised task (also called a _pretext task_). In such a task, \"self-supervised labels\" can be generated in an automatic and convenient way, without any intervention of, or effort required from, human manual labeling. Examples of self-supervised pretext tasks are given in the remaining parts of this paper. To the best of our knowledge, we present in this paper a first study in the literature on employing the self-supervised learning approach to solve the CG forensics problem. Both existing representative self-supervised learning methods and a properly-designed new method are utilized to carry out the forensic classification between computer-graphics images and natural images. More precisely, in our proposed pretext self-supervised task, the objective is to correctly classify authentic images and their various modified versions after applying different kinds of image manipulation operations. Experimental results show that our proposed method, with a new and appropriate pretext task oriented to image forensics, achieved satisfactory and better performance than existing methods. Our contributions are summarized as follows. * We conduct and report, to our knowledge, a first and comprehensive study on utilizing self-supervised learning for CG forensics. * We propose a new self-supervised learning method, with an appropriate pretext task, for the distinction between CG images and NIs. Different from existing pretext tasks,  
Figure 1: Examples of CG images and NIs. The two CG images on the top row were generated respectively by the VRay [9; 10] and Corona [11] rendering engines, and the two NIs on the bottom row are, respectively, from the VISION [12] and RAISE [13] datasets.