## 6 Experiment and Result Analysis
### Comparative Experimental Analysis
#### 6.7.1 Comparison Experiment
Importantly, as illustrated in Table 11, our proposed method achieves significant detection performance improvements over backbone networks like EfficientNetB4 and LRNet, owing to the integration of texture features and guided inter-frame correlations. The method proposed in this paper consistently outperformed other methods across all four datasets. This confirms that the multi-feature decision fusion approach proposed here compensates for deficiencies in feature extraction found in other methods, resulting in highly effective detection models that integrate both inter-frame and intra-frame network branches. In summary, this study validates the superiority of our method in the field of deepfake detection, achieving state-of-the-art performance levels. Based on comparative experimental results, we have selected several methods to illustrate the AUC and ACC values obtained on the Deepfakes, Face2 Face, FaceSwap, and NeuralTextures datasets in the form of line graphs, as depicted in Figure 20.  
Figure 20 presents line graphs of AUC and ACC values tested on four different datasets of deepfake faces. Figures 20a and 20b illustrate the ACC and AUC performances of various methods across these datasets, respectively. Based on the ACC and AUC detection results, it is shown that the lines representing our method are generally positioned above those of other methods. Therefore, the calculated AUC and ACC values are relatively high, indicating that our approach combines facial geometry and texture features specific to deepfake datasets, resulting in superior detection performance. To comprehensively evaluate model reliability, a dual-metric framework incorporating both False Positive Rate (FPR) and False Negative Rate (FNR) was implemented to systematically quantify classification error patterns across four major deepfake categories:  
Figure 20: Line graphs comparing the AUC and ACC of various specific algorithms across different datasets. (**a**) Line graphs of ACC for different methods across four datasets. (**b**) Line graphs of AUC for different methods across four datasets. Deepfakes, Face2 Face, FaceSwap, and NeuralTextures. As detailed in Table 12, the error distribution was rigorously analyzed under a fixed decision threshold (\(\theta\) = 0.5). As evidenced in Table 12, statistically significant disparities in False Positive Rate (FPR) and False Negative Rate (FNR) were observed across the four deepfake datasets. The Deepfakes category demonstrated superior error control performance, achieving the lowest FPR (0.32%) and FNR (0.51%), while NeuralTextures also maintained relatively low error rates (FPR = 0.41%, FNR = 0.63%). Notably, the elevated FPR in FaceSwap (1.12%, 2.1\(\times\) higher than the category average) is attributed to morphological similarities between synthetic mouth deformations and authentic smile expressions. Conversely, Face2 Face exhibited heightened sensitivity to low-resolution inputs, resulting in a marked FNR of 0.89%. Analysis revealed positive relationship between FPR and FNR, suggesting bias patterns that may originate from shared adversarial feature distributions.