## 4 Experimental Results
### Discussion  
The experimental results presented in this section showed that existing self-supervised methods, mostly designed for image content understanding, could, in some cases, reach rather satisfying performances for the main task of discriminating between CG images and NIs, but better results were achieved by our proposed method, especially in challenging scenarios with data scarcity and/or a more difficult forensic problem (e.g., when dealing with a multi-class CG forensic problem with few data available for fine-tuning). This was probably due to the better appropriateness of the self-supervised pretext task in our method, having the objective, as discussed in Section 3, to capture different kinds of deviations from NIs. This design was reasonable because, intuitively, CG images are a group of synthetic images that deviate, though sometimes subtly, from NIs. As shown in Section 3, the manipulation operations considered in our pretext task could introduce different types of visual distortions in the spatial domain (Figure 2), as well as different kinds of changes in the image pixel value histogram (Figure 3) and the joint distribution of neighboring pixels (Figure 4). Learned representations that were sensitive to these various modifications would be useful for detecting the subtle deviation of CG images from NIs. Our method had lower CG forensics accuracy than the state-of-the-art fully supervised method ENet in the majority of tested scenarios. This was rather understandable, and in line with the observation that early self-supervised methods for computer vision tasks have clearly lower performance than their supervised counterparts. Nevertheless, our method was able to achieve comparable, or even better. performance compared with ENet in the following three scenarios: the fine-tuning (on 20% data) binary classification scenario with the same number of training epochs (as mentioned towards the end of the first paragraph of Section 4.3), the challenging multi-class CG forensic problem with fine-tuning on 20% data (as shown in the last column of Table 5), and the same multi-class fine-tuning setting but with our method pre-trained on more data (as shown in the last column of Table 6, our method also clearly performed better). These results implied that our self-supervised method would have a good chance to outperform supervised methods when facing a difficult forensic problem and/or when few labeled samples were available. In addition, extensive self-supervised training is also beneficial if we can afford the time and computational costs. In our opinion, the experimental results of our method are encouraging, especially considering that this was. to our knowledge, the first attempt in the literature on self-supervised learning for CG forensics, and we think that our method would be useful in challenging practical cases; for instance, in which we have very few labeled data and/or we have a good number of possible sources of CG images (from different rendering engines) or even NIs (from different cameras) to identify. We believe that our self-supervised method, or its improved version, has the potential to reach high flexibility and good practical applicability in real-world forensic scenarios. Furthermore, it appears \begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Fine-Tune on Whole Train Set** & **Fine-Tune on 20\% Train Set** \\ \hline RotNet [38] & 79.83\% & 62.89\% \\ Jigsaw [41] & 82.17\% & 66.56\% \\ BarlowTwins [46] & 85.50\% & 73.78\% \\ SimCLR [42] & 85.78\% & 73.44\% \\ MoCo-V2 [44] & 86.33\% & 73.89\% \\ ImageNetClassi & 82.22\% & 66.00\% \\ ENet adapted (supervised) & 90.61\% & 71.06\% \\
**Ours** & 89.22\% & 79.56\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Test accuracy of different methods under the fine-tuning evaluation protocol for the 5-class classification problem of CG forensics (the 5 classes being NI, CG of Artlantis, CG of Autodesk, CG of Corona, and CG of VRay).