## 4 Experimental Results
### Additional Experiments
#### 4.4.1 Impact of Manipulations  
Experiments were conducted to study the impact of the pretext task design in our method. As presented in Section 3.2, we adopted a pretext task of 9-class classification of NIs and their modified versions, obtained by applying eight different manipulations. Here we compared with two other versions of pretext task, respectively, with 6 classes and 23 classes. In the version with 6 classes, we removed from the 9-class version the three manipulations of histogram equalization, pixel value quantization and Gamma correction. To get the version with 23 classes, we expanded the 9-class version with more variants for some manipulations. For example, in the 23-class version the Gamma correction manipulation had two ranges for the correction factor, i.e., between 0 and 1 (resulting in a brighter image after correction) and between 1 and 2 (resulting in a darker image). For the noise addition manipulation, we also considered the three variants with added noise only in one of the three color channels. Our purpose was to have a first and rough idea about the impact of the number of manipulations and their subdivisions with more parameters/settings on the performance of the main task of CG forensics. We tested the performance of these three pretext tasks (i.e., 6-class, 9-class and 23-class versions) under the linear and the fine-tuning (on 20% training set) evaluation protocols. The performance comparisons are illustrated in Figure 5. We can observe that, in general, the 9-class version had consistently better performance than the 6-class version (except on Corona under the linear evaluation protocol where the performance was equal), while the 23-class version did not clearly perform better than the 9-class version and, sometimes, the performance of 23-class pretext task could be lower than that of the 9-class task (e.g., on Autodesk and VRay under the linear evaluation protocol). One possible explanation was that, in order to get a better CG forensics performance, the diversity of considered manipulation operations (i.e., the difference between the 9-class and 6-class versions), would be more important than the subdivision of manipulations with different parameters/settings (i.e., \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] & 61.39\% & 50.11\% \\ Jigsaw [41] & 65.61\% & 53.22\% \\ BarlowTwins [46] & 44.94\% & 33.22\% \\ SimCLR [42] & 66.50\% & 51.39\% \\ MoCo-V2 [44] & 72.06\% & 57.33\% \\ ImageNetClassi & 70.28\% & 54.61\% \\ ENet adapted (supervised) & 90.61\% & 71.06\% \\
**Ours** & 83.22\% & 71.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Test accuracy of different methods under the fine-tuning evaluation protocol for the 5-class classification problem (the 5 classes being NI, CG of Artlantis, CG of Autodesk, CG of Corona, and CG of VRay). The presented results are classification accuracies on the testing set of a dataset properly constructed from the datasets of Artlantis, Autodesk, Corona and VRay (_cf._ the main text for details). the difference between the 23-class and 9-class versions). The observations gained from this set of experiments would be useful for our future work on further experimental studies in this direction, as well as on the design of more appropriate pretext tasks for image forensics applications. For instance, and as mentioned above, it appears that increasing the diversity and types of manipulation operations would be more favorable than subdividing one manipulation into several with different intensities. In light of this observation, our proposed self-supervised pretext task could be further improved by seeking and including more kinds of diverse manipulation operations, or even by attempting to automatically derive the \"optimal\" set of manipulations (e.g., with inspiration gained from the method proposed in [55]). In addition, one may be interested in the relative importance of manipulations on the performance of CG forensics.