# Lightweight Deepfake Detection Based on Multi-Feature Fusion
## 4 Implementation
### Results and Discussion  
The results are further provided and analyzed in depth. This study proposed a feature-level fusion of LBP, HOG, and KAZE features for classification via the FaceForensics++ and Celeb-DF datasets. The evaluation of the results on the basis of the provided validation is presented below: Table 2 presents the classification accuracies obtained using various classifiers with various feature sets, including LBP alone, KAZE alone, and the fusion of LBP, HOG, and KAZE features. The experiment was conducted on the FaceForensics++ and Celeb-DF datasets to evaluate the effectiveness of these features in distinguishing between genuine and manipulated visual content/deepfake. Analyzing the results shows that both LBP and KAZE features individually perform well across different classifiers when tested on the FaceForensics++ dataset. LBP features demonstrated their effectiveness in texture-based analysis, achieving an accuracy of 71.22% with Extra Trees and 70.76% with Random Forest classifiers. Similarly, KAZE features, which focus on detecting structural alterations and keypoint variations in images, produced accuracies ranging from 75.70% with Random Forest to 86.12% with a Support Vector Classifier. HOG features also showed strong performance, with accuracies between 85.76% and 92.12% using a Support Vector Classifier. This result is close to the benchmark accuracy of 94.44% for the FaceForensics++ dataset, which was achieved by EfficientNet [28]. These findings highlight the potential of integrating feature extraction techniques to improve deepfake detection performance. However, the most notable results were achieved by the fusion of HOG and KAZE features, demonstrating a clear advantage over individual feature sets. This fused approach showed superior performance across all classifiers, with accuracy rates of 91.12% using Extra Trees, 92.12% with the Support Vector Classifier, and an impressive 94.44% when tested with the state-of-the-art EfficientNet. These results indicate that integrating texture-based (i.e., HOG) and keypoint-based (i.e., KAZE) features significantly improved the model's ability to detect deepfake content. These features allow for more effective detection of deepfakes compared to using either method alone (see Figure 3 for a detailed visualization). \begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{2}{c}{**Fusion of Features with Classifiers**} & **Accuracy** \\ \hline \multirow{2}{*}{LBP Features} & Extra Trees Classifier & 71.22\% \\ \cline{2-3}  & RF Classifier & 70.76\% \\ \hline \multirow{4}{*}{KAZE Feature} & Extra Trees Classifier & 85\% \\ \cline{2-3}  & Support Vector Classifier & 86.12\% \\ \cline{2-3}  & RF Classifier & 75.70\% \\ \cline{2-3}  & XGB Classifier & 65.29\% \\ \hline \multirow{4}{*}{HOG + KAZE Feature} & Extra Trees Classifier & 91\% \\ \cline{2-3}  & Support Vector Classifier & 92.12\% \\ \cline{2-3}  & RF Classifier & 85.70\% \\ \cline{2-3}  & XGB Classifier & 83.19\% \\ \hline \multirow{4}{*}{LBP + KAZE Features} & Extra Trees Classifier & 82.61\% \\ \cline{2-3}  & Support Vector Classifier & 86.22\% \\ \cline{2-3}  & RF Classifier & 85.54\% \\ \cline{2-3}  & XGB Classifier & 88.56\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy of fusion of LBP, HOG, and KAZE features with FaceForensic++. Future research could focus on strengthening the fusion technique of HOG and KAZE features to improve feature selection and reduce dimensionality. Another promising direction is the exploration of DL architectures, such as CNNs, to gain deeper insights into hierarchical feature representations and further boost classification accuracy. Additionally, evaluating the proposed fusion model on larger and more diverse datasets other than FaceForensics++ would help assess its robustness in real-world scenarios. These advancements would play an important role in strengthening deepfake detection, particularly in addressing the growing challenges in digital manipulation and deepfake technologies.