# Novel Deep Learning-Based Facial Forgery Detection for Effective Biometric Recognition
## 4 Discussion  
The experimental outcomes clearly demonstrate the efficacy of the proposed deep learning network in detecting facial forgeries. Notably, the texture-focused approach enables the system to capture subtle anomalies often overlooked by conventional methods. The comparative analysis (Table 7) highlights a substantial improvement over previous approaches, with the proposed algorithm achieving an ACER of 0.06.  
The databases used play a crucial role in achieving these results. CASIA-FASD provides diverse attack variations, allowing the model to learn from a range of forgery scenarios. CelebA-Spoof contributes with its vast variety of environmental and forgery attributes,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification** & **ACER** & **Accuracy** & **Precision** & **Recall** & **f1-Score** \\ \hline Genuine & & & & \\ Forged & 0.15 & 0.89 & 0.94 & 0.92 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The experimental results of the proposed algorithm regarding replay attack as test dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **ACER** \\ \hline One-class adaptation [15] & 1.69 \\ Learnable gradient operator [16] & 0.8 \\ Multi-domain [29] & 2.6 \\ Simulating spoofing clues [30] & 2.33 \\ multi-regional [31] & 1.3 \\
**proposed** & **0.06** \\ \hline \hline \end{tabular}
\end{table}
Table 7: The ACER of the proposed algorithm compared to the previous studies.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & **ACER** & **Accuracy** & **Precision** & **Recall** & **f1-Score** \\ \hline Genuine & & & & & \\ Forged & 0.10 & 0.93 & 0.74 & 0.85 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The experimental results of the proposed algorithm regarding print attack as test dataset.  
further enhancing model robustness. NIA-ILD, with its real-world liveness detection conditions, ensures that the model generalized well to practical scenarios. These diverse datasets enable the model to adapt effectively to unseen forgery methods, as validated through Leave-One-Out Cross-Validation experiments.  
Despite the superior performance, certain factors might have influenced the outcomes. The balance and diversity of the datasets significantly contribute to the model's learning capability. However, variations in lighting conditions, device types, and environmental factors could have introduced biases. Addressing these potential biases in future research will be critical for improving the model's adaptability.  
Furthermore, computational efficiency is achieved by employing a network architecture with fewer layers but wider feature maps, ensuring that the system can be implemented in real-time applications without significant hardware constraints. The binary classification approach not only simplifies the training process but also enhances the model's ability to generalize to new types of forgeries.  
This research was supported by Hallym University Research Fund, 2024 (HRF-202410-004).  
This research was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Hallym University (protocol code HIRB-2024-108 and date of approval: 8 January 2025).  
Informed Consent Statement: Informed consent has been obtained from all subjects involved in the study.  
All the data used in this study can be found and downloaded at CASIA-FASD [19], CelebA-Spoof [21], and NIA-ILD [22] by appropriate requests.  
The author declares no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.