## 4 Experimental Results
### Fine-Tuning Evaluation Protocol  
A more practical, and more important, assessment methodology for self-supervised learning methods is the fine-tuning evaluation protocol [8, 51]. Different from the linear evaluation protocol in the last subsection, all parameters of neural network obtained after self-supervised pre-training (with a new and suitable classification layer for the main task) are fine-tuned on training samples of the main task. This reflects the real practical utility of learned pre-trained neural networks for solving our target problem of CG forensics. Table 3 presents the obtained results when the fine-tuning was carried out on the whole training set of the four CG forensics datasets (i.e., fine-tuned on 5040 CG images and 5040 NIs). Again, the data and the parameters of fine-tuning were the same for all self-supervised methods. Besides the results of our method and the comparison methods considered in the last subsection, we also provide, in Table 3, the result of a state-of-the-art fully-supervised method called ENet [27]. It can be observed that performances of self-supervised methods, both ours and existing ones, were, in general, largely improved when compared to those of the linear classification protocol. Our method still outperformed existing self-supervised methods. There was a small performance gap between our self-supervised method and the fully-supervised ENet method; this was rather understandable, because ENet is a specially designed neural network, having architecture and training well tailored for solving the CG forensics problem in a fully-supervised setting. Moreover, in order to test the utility of our proposed method in a challenging data scarcity scenario, we also conducted tests under the setting where pre-trained networks were fine-tuned only on 20% of the training set of the four CG forensics datasets (i.e., fine-tuned on 1008 CG images and 1008 NIs), and the obtained results are presented in Table 4. We can observe that test accuracies in this challenging, yet practical, situation decreased, and our proposed method had the least decrease (test accuracy remained higher than 92% on all the four datasets under this challenging data scarcity scenario). Our method again outperformed existing self-supervised methods and the network pre-trained on the ImageNet classification task (denoted by ImageNetClassi). In addition, the performance gap between our method and the fully-supervised ENet became slightly smaller (the average of the difference of performances between the two methods on the four datasets changed from 1.97% in Table 3 to 1.59% in Table 4). This was, in our opinion, mainly due to the effective pre-training with an appropriate pretext task and on a good number of data for our self-supervised method. Please also note that in order to reach the performances shown in Tables 3 and 4, ENet (with default training parameters as suggested in [27]) took longer training epochs than the fine-tuning of our method (i.e., 300 epochs for ENet vs. 105 epochs for ours). If ENet was trained with the same number of 105 epochs as our method, its test accuracy dropped to 93.47%, 90.83%, 91.39% and 94.58%, respectively, for the four datasets. In this case, our \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Artlantis** & **Autodesk** & **Corona** & **VRay** \\ \hline RotNet [38] & 82.64\% & 84.44\% & 79.44\% & 83.61\% \\ Jigsaw [41] & 84.31\% & 87.64\% & 83.19\% & 86.39\% \\ BarlowTwins [46] & 75.14\% & 80.00\% & 76.11\% & 80.83\% \\ SimCLR [42] & 86.25\% & 79.17\% & 86.94\% & 87.22\% \\ MoCo-V2 [44] & 88.89\% & 81.53\% & 88.47\% & 90.00\% \\ ImageNetClassi & 87.08\% & 90.28\% & 85.69\% & 89.58\% \\
**Ours** & 92.50\% & 94.86\% & 92.08\% & 91.81\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test accuracy of different methods under the linear classification evaluation protocol. The presented results are classification accuracies on the testing set of the four CG forensics datasets of Artlandis, Autodesk, Corona and VRay.