## 4 Experimental Results
### Additional Experiments
#### 4.4.1 Impact of Manipulations
In practice, we found that the relevant study was rather complicated, because the performance depended on a number of factors, e.g., the considered dataset. For instance, in a set of preliminary experiments, we compared the performance of jointly modifying color saturation and color hue within one manipulation (as in our pretext task, _cf._ Table 1 row of \"Color jitter\") and that of separately applying modification on color saturation and on color hue as two manipulations. The obtained forensic accuracies under the fine-tuning evaluation protocol (with 20% training data) on the four datasets of Artlantis, Autodesk, Corona, and VRay were, respectively, 96.25% vs. 94.44%, 94.31% vs. 93.47%, 93.06% vs. 94.58%, and 92.64% vs. 93.47%, with the former number for joint modification and the latter one for separate modification. We can observe that there was no clear trend in these experimental results (although joint modification, as used in our 9-class pretext task, had slightly higher overall accuracy averaged on four datasets): the joint modification performed better on Artlantis and Autodesk, while the separate modification performed better on Corona and VRay. In another set of experiments, we compared two variants of 6-class pretext task, i.e., the first one with the removal of histogram equalization, pixel value quantization and Gamma correction from the 9-class pretext task (the same as in the previous paragraph) and the second one with the removal of Gaussian blurring, noise addition and sharpness enhancement. The obtained test accuracies under the fine  
Figure 5: Comparisons of CG forensics performances of different versions of self-supervised pretext tasks, respectively, with 6 classes, 9 classes and 23 classes (_cf._ the main text for details). The comparisons were carried out on the four CG forensics datasets of Artlantis, Autodesk, Corona and VRay, under the linear evaluation protocol (left panel) and the fine-tune evaluation protocol (right panel). The vertical axes in the two panels represent the classification accuracy (in percentage) on testing set. The results in the two panels were not directly comparable, because the amount of training samples was different (whole training set for the left panel and 20% training data for the right panel). tuning evaluation protocol (with 20% training data) on the four datasets were, respectively, 92.22% vs. 93.47%, 93.19% vs. 94.17%, 92.92% vs. 91.81%, and 91.94% vs. 91.94% (the former for the first variant, while the latter was for the second variant; a minor side remark here is that both 6-class variants had lower accuracy than the 9-class pretext task on all four datasets). We can observe that the second variant had better performance on Artlantis and Autodesk, while the first variant worked better on Corona, and that the performance was equal on VRay. It still remained difficult to assess the relative importance of the manipulations of histogram equalization, pixel value quantization and Gamma correction (i.e., straightforward modification of pixel values) and the manipulations of Gaussian blurring, noise addition and sharpness enhancement (i.e., rather frequency-oriented operations). Although facing a complicated research problem, we plan to make efforts in future studies to better understand these experimental observations, as well as the impact and relative importance of various manipulation operations. This would also allow us to gain deeper understanding of the real difference between CG images and NIs.