# Novel Deep Learning-Based Facial Forgery Detection for Effective Biometric Recognition
## 2 Materials and Methods
### Proposed Algorithm  
#### 2.2.1 Classification and Control Criteria  
One of the significant challenges in combating facial forgery, particularly with the rise of deepfakes, lies in achieving a balance between specificity and generalizability in deep learning network design. Overly specific classification criteria, while offering detailed information on the forgery method, can be susceptible to overfitting and struggle to adapt to emerging forgery techniques. Conversely, overly broad criteria may lack the necessary discriminatory power to effectively distinguish genuine from forged images.  
This paper proposes a novel approach that addresses this challenge by adopting a binary classification strategy. This approach focuses solely on differentiating between authentic and forged images, bypassing the need to categorize specific acquisition methods or individual facial forgery attack techniques. By employing a binary classification framework, the deep learning network can concentrate on learning the core characteristics that differentiate genuine and forged facial data.  
This design choice offers several key advantages. First, it reduces overfitting risk. By focusing on a binary classification task, the model complexity is reduced, mitigating the risk of overfitting to the training data. This allows the network to generalize more effectively to unseen examples, including potential future forgery methods. Second, it enhances adaptability to evolving forgery techniques. The binary classification approach prioritizes the identification of core forgery signatures, regardless of the specific manipulation method employed. This enables the network to adapt and detect new forgery techniques, even  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **CASIA-FASD** & **CelebA-Spoof** & **NIA-ILD** \\ \hline number of images & 8126  & 625,537 & 1,624,500 \\ image type & JPG & PNG, JPG & JPG \\ resolution  & 512 \(\times\) 512 & 512 \(\times\) 512 \\ forgery methods & print, papercut, replay & print, papercut, replay, 3D & print, replay, 3D \\ \hline \end{tabular}  
if they differ in their technical details from those encountered during training, as long as they share fundamental characteristics with previously observed forgeries. Third, it achieves computational efficiency. A binary classification task requires a simpler final layer with only two output nodes, compared to a multi-class approach with numerous output nodes. This translates to a more computationally efficient model, facilitating faster training and deployment.  
The final layer of the proposed deep learning network utilizes a Softmax activation function. This function ensures that the network's output probabilities sum to one, providing a clear distinction between the genuine and forged image classes. This paper advocates for a binary classification approach in deep learning networks for facial forgery detection. This strategy prioritizes generalizability and adaptability to emerging forgery methods, while maintaining effectiveness in distinguishing authentic from manipulated facial data. By striking a balance between specificity and generalizability, the proposed approach offers a robust and future-proof solution for combating the evolving threat of facial forgery.