# Novel Deep Learning-Based Facial Forgery Detection for Effective Biometric Recognition
## 1 Introduction
Despite the advancement of technology, which enables more diverse and sophisticated facial forgery methods, research has not been able to keep pace with these developments. Proactive research and responses are needed to address this issue. The accuracy and reliability of deep learning results are significantly influenced by the quality and quantity of the data used for training. A key bottleneck in deep learning research is securing a sufficient amount of high-quality data, as this directly impacts the performance of models. Without adequate and well-prepared data, even the most advanced \begin{table}
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**Types** & **Techniques** & **Use Cases** & **Analysis** \\ \hline GAN (Generative Adversarial Network) Attacks & The generator model creates fake faces similar to real faces, and the discriminator model distinguishes between fake and real faces & Identity theft, fake profile creation, malware injection, etc. & GAN attacks can generate more sophisticated fake faces than Deepfake attacks, and defense techniques against them require more advanced technology. \\ \hline Presentation Attacks & 2D image attacks, 3D mask attacks, replay attacks, etc. & Illegal access, financial fraud, personal information leakage, etc. & Presentation attacks are constantly evolving, and defense techniques must also evolve to counter new attack methods. \\ \hline \end{tabular}
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Table 2: _Cont._algorithms can underperform, leading to inaccurate or biased predictions. In classification tasks, achieving a balance in the number of data samples across different categories is crucial. Imbalanced data can skew the learning process, causing the model to favor the majority class and neglect the minority class, resulting in poor generalization and inaccurate predictions. Therefore, both data quality and balance are fundamental to deep learning and essential for producing objective and reasonable results. The importance of these factors cannot be overstated, as they form the foundation for reliable and fair analysis in machine learning systems. Data imbalance refers to the situation where the number of data samples in certain classes is significantly smaller than in other classes. This issue can negatively impact the training of machine learning models, particularly by reducing the prediction performance for the minority class. Models trained on imbalanced data tend to be biased toward the majority class, as they encounter it more frequently during training. This can result in poor generalization and inaccurate predictions for the minority class. Data imbalance is a common problem across various domains, but it is especially prevalent in the following areas [17]:  
1. Medical Field: Medical datasets often suffer from imbalance because rare diseases or abnormal findings are, by nature, scarce. For example, images of rare medical conditions or abnormal test results are much harder to collect than those of normal or healthy cases. This disparity can significantly affect the diagnostic accuracy of deep learning models. Models may become highly adept at identifying common conditions but fail to accurately diagnose rare or critical conditions, which can lead to harmful misdiagnoses. 2. Finance: In fraud detection or credit risk prediction, fraudulent transactions or cases of high credit risk occur far less frequently than normal transactions. This imbalance creates challenges in developing models that are sensitive enough to detect fraudulent behavior without overfitting to the majority (normal transactions), where the vast majority of data resides. This imbalance can severely compromise the model's ability to detect fraudulent activities, which are often the most critical to identify. 3. Cybersecurity: In areas such as malware detection, intrusion detection, or DDoS (Distributed Denial of Service) attack prevention, the amount of malicious activity data is typically much smaller compared to normal network traffic data.