## 5 Conclusions and Future Work  
In this paper, we presented, to our knowledge, the first study in the literature on the utility of self-supervised learning for the discrimination of CG images and NIs. We proposed a new self-supervised learning method with a forensics-oriented pretext task in which the objective was to correctly classify authentic images and their modified versions obtained after applying different kinds of manipulations. The self-supervised pre-training was carried out without leveraging any human manual labeling effort and without using any CG image. Experimental results under different evaluation scenarios demonstrated that our method coped better with the CG forensics problem than existing self-supervised methods. In a scenario with data scarcity and a difficult forensic problem, our self-supervised method could achieve comparable, or even better, performance compared with a specially designed supervised method. We hope that this first attempt on self-supervised CG forensics could encourage more research efforts in this interesting and promising working direction. One limitation of our method was that there was still a performance gap when compared with a state-of-the-art fully supervised method, especially under situations with a relatively easy binary classification problem and with sufficient training data. This, however, remains understandable, and is similar to the existence of performance gap between early self-supervised methods and their supervised counterparts for computer vision applications. In order to enhance the practical utility of our method, we plan to conduct studies on the improvement of the learning capacity of the self-supervised framework, e.g., by combining forensics-oriented pretext task learning with the idea of metric learning related to the perceptual quality of images. Our proposed method makes the assumption (though quite reasonable) that only NIs are available for the self-supervised learning. If CG images are also available, we might make use of these images to further improve the model obtained after self-supervised learning with an additional training step, in an attempt to make the model more adapted to the data and problems that may be encountered in real-world applications. In this case, we could consider also applying manipulations on CG images, either as an effective data augmentation technique (i.e., processed CG images as augmented and additional training data), or in a contrastive learning framework (e.g., two augmented versions of a CG image should have consistent features and prediction results). In a broad sense, we would like to carry out studies on the design of better self-supervised methods for CG forensics, with a more suitable pretext task or even with a brand-new learning scheme. For example, it might be interesting to derive pretext tasks and self-supervised learning schemes related to the statistical modeling of natural images or to the perception of visual appearance of authentic and degraded images. The combination of these different self-supervised learning schemes, e.g., within a multi-task learning framework, would allow us to achieve improved performance for the discrimination of CG images and NIs. Finally, we would like to explore the utility of the self-supervised learning mechanism for other related applications, including the detection of image forgeries (e.g., splicing, copy-move and inpainting) and the assessment of visual or aesthetic quality of digital images.  
This work was partially funded by the French National Research Agency grant number ANR-15-IDEX-02.  
Data Availability Statement: Not applicable.  
The author would like to thank NVIDIA for a GPU gift.  
The author declares no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.