# Lightweight Deepfake Detection Based on Multi-Feature Fusion
## 4 Implementation
### Results and Discussion
The results presented in Table 3 highlight the classification accuracy of different feature extraction techniques when applied to deepfake detection using the Celeb-DF dataset. The findings demonstrate that combining multiple feature descriptors improves detection performance compared to using individual features alone. Among the single-feature approaches, LBP features with a Support Vector Classifier (SVC) achieved the highest accuracy (72%), which yielded a lower accuracy of 68%. This suggests that LBP is more effective in capturing local texture variations relevant to distinguishing real from fake images. When feature fusion was applied, the combination of HOG and KAZE features achieved the highest accuracy (78%), showing a significant improvement over individual features. This indicates that integrating gradient-based descriptors (HOG) with keypoint-based features (KAZE) provides a more comprehensive representation of image characteristics, improving classification reliability. Similarly, LBP combined with KAZE achieved an accuracy of 75%, further confirming that KAZE features contribute positively to deepfake detection by enhancing feature diversity. Figure 3: Outcome of the proposed and ML algorithms in terms of accuracy (i.e., RF, extra trees, and SVC). Overall, these results demonstrate that fusion strategies, particularly HOG + KAZE, are more effective than single-feature approaches in deepfake detection on the Celeb-DF dataset. The findings suggest that feature extraction techniques can improve the robustness of detection models, making them more resilient to sophisticated deepfake manipulations. Future research might investigate refining feature selection and classifier tweaking to improve overall performance. Table 4 presents a comparison of execution times for feature extraction and classification, measured in milliseconds on both GPU and CPU. This comparison evaluates the inference time and classification accuracy of different methods, as outlined in the evaluation criteria (Section 4.1), using the FaceForensics++ dataset. Feature extraction time reflects the duration required to extract LBP, HOG, and KAZE features, in contrast with the extraction times of traditional machine learning models. Training time refers to the time duration needed to train the classifier after feature extraction, while inference time measures the time taken to classify a single instance once the model has been trained. This analysis provides insights into the computational efficiency of various approaches, helping to assess their suitability for real-time deepfake detection. These tables provide a clear comparison of the efficiency and effectiveness of the proposed fusion model using KAZE and HOG features against traditional ML approaches. Table 4 demonstrates that while feature extraction with KAZE and HOG may take slightly longer (per frame) than traditional ML models on GPU, it is significantly faster than training DL models. Table 2 highlights that the proposed fusion model achieves competitive accuracy while maintaining comparative training and inference time, making it suitable for real-time applications where speed is important (see Table 4 for GPU and CPU inference). These comparisons underscore the practical advantages of the proposed fusion model in terms of computational efficiency without compromising classification performance. The proposed fusion-based methods perform better in terms of training time, despite CNN's marginally better performance and accuracy, but are lacking in inference time, especially when the CPU is used. Although CNN's inference is somewhat superior when the GPU is used compared to the proposed fusion model, the methods are reliable and perfect for resource-constrained situations, as they performed better when the CPU is used. The