# Deep Learning Methods to Detect Image Falsification
## 3 Materials and Methods  
Method chosen as a basis for research and improvement is presented in [8]. This approach, named LRNet, is more effective than existing deepfake detection methods, which mainly rely on visual appearance but can be easily circumvented by complex image manipulations. The method used calibrated facial feature data extracted from the videos, which were inserted into a sequence of two types of vectors and then used in a two-stream RNN model. The g1 stream was used to detect changes in facial shape, whereas g2 was used to find differences in facial features. After the RNN output, a fully connected layer was added to determine whether the video was fake or not. The average of these two stream results indicated the final result. To detect fake videos, each video example was divided into fixed-length short videos, and then the result was obtained from these short videos to determine whether the video was fake or not. The presented method used the FaceForensics++ dataset. [PERSON] et al. [15] introduced the FaceForensics++ dataset as an extension of the original FaceForensics [3] dataset, which focused solely on expression manipulation. This dataset comprised 1000 real videos extracted from YouTube and 1000 manipulated videos generated using computer graphics and GAN methods for each type of manipulation. The dataset included videos at three different compression levels: raw, c23, and c40. Information about the dataset is presented in Table 2. The FaceForensics++ dataset consisted of four different manipulation subsets and one pristine subset. The authors of [8] adopted an 8:2 dataset split. LRNet with raw compression level produced an accuracy of 99.7% and an AUC of 99.9%, whereas the c23 compression level had an AUC of 97.3%, and the c40 level had an accuracy of 91.2% and an AUC of 95.7%. To compare the results of their study more effectively, we reproduced the training of their model using different computer hardware and added additional metrics, including Precision, Recall, F1, and time in seconds. Hardware used in the training:  
* Graphics card--NVIDIA GeForce RTX 3070 Laptop GPU 8 GB GDDR6;
* Processor--AMD Ryzen 9 5900 HX;
* RAM--24 GB 3200 MHz;
* Storage disk--SSD CT1000P2 SSDS 1 TB. Table 3 presents the replicated results from LRNet. For the raw compression level, we achieved an accuracy of 95.71% and an AUC of 97.87%. For the c23 level, we obtained an AUC of 96.81%, and, for the c40 level, we achieved an accuracy of 91.28% and an AUC of 93.71%. These results were slightly lower than those reported in the original paper. Inherent variability is a fundamental aspect of machine learning experiments, which can result in slight differences in results even when following the same procedures and utilizing the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Images for** & **Images for** & **Images for** \\  & **Training** & **Verification** & **Testing** \\ \hline Pristine (no fake images) & 367,282 & 68,862 & 73,770 \\ DeepFakes (generated fakes) & 367,282 & 68,862 & 73,770 \\ Face2 Face (generated fakes) & 367,282 & 68,862 & 73,770 \\ FaceSwap (generated fakes) & 292,376 & 54,630 & 59,672 \\ NeuralTextures (generated fakes) & 292,376 & 54,630 & 59,672 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Details of FaceForensics++ dataset [15]. same datasets. Additionally, we used different hardware and environments, which can also impact the results and introduce discrepancies. However, the discrepancies between our reproduced results and the original findings fall within a reasonable range. We then conducted an experiment to test the impact of changing these model parameters:  
* Block size: the length of video in frames, with possible options of 30, 60, and 120 frames. The initial model used 60-frame video clips;
* Dropout rate: the percentage of features to be dropped out, with possible options of 0.3, 0.4, 0.5, 0.6, and 0.7. The initial model had a dropout probability of 0.5;
* Learning rate: with possible options of 0.001, 0.005, and 0.01.