# Novel Deep Learning-Based Facial Forgery Detection for Effective Biometric Recognition
## 2 Materials and Methods
### Proposed Algorithm
#### 2.2.2 Proposed Deep Learning Network  
Following a rigorous review of the acquired facial image dataset and a comprehensive analysis of the features present in forged samples, a critical observation emerged. While the presence of eyes, nose, mouth, and ears play a role in facial recognition, the texture of the facial surface proved to be a far more reliable indicator of forgery. The term \"texture\" refers not to specific objects within an image but to signals characterized by relatively high-frequency noise levels distributed across the entire image. This textural information encompasses characteristics such as roughness, color variations, pattern type, size, frequency of occurrence, and the level of randomness or periodicity within the patterns [24,25]. This pivotal insight informed the design of a novel deep learning network specifically optimized for texture analysis. The network architecture prioritizes the extraction of these textural features, enabling it to effectively distinguish between genuine and forged facial images. The two key design choices are shown below. * Focus on Texture over Objects: By prioritizing texture analysis, the network is less susceptible to inconsistencies in the rendering of individual facial features, which can sometimes be meticulously crafted in forgeries. Texture, on the other hand, is often more challenging to replicate perfectly, especially for forgeries generated using less sophisticated techniques. * Small Number of Layers with Wide Feature Maps: To achieve efficient implementation and reduce computational complexity, the network employs a relatively shallow architecture with a smaller number of layers. However, to compensate for the reduced depth, these layers utilize wider feature maps. Wider feature maps allow the network to capture a richer and more nuanced representation of the textural information within the images. This combination of a focus on texture analysis and a network architecture optimized for efficiency ensures that the proposed solution can effectively detect forgeries while maintaining a practical implementation footprint. The network's ability to prioritize these subtle textural cues, often overlooked by traditional forgery detection methods, empowers it to achieve a high degree of accuracy in distinguishing genuine from forged facial data. One of the promising solutions that addresses the data imbalance issue is _supervised contrastive learning_[26]. Supervised contrastive learning is a contrastive learning technique for representation learning, particularly applied to images. The algorithm positions different data points of the same class closely in the embedding space while pushing data points of different classes farther apart. In other words, it organizes the embedding space based on class labels, enabling efficient and accurate learning, and thereby demonstrating superior performance compared to traditional methods. The algorithm ensures that, within the embedding space, data points belonging to the same class are drawn closer, whereas data points from different classes are repelled from each other. When classification is complete, this approach establishes an embedding space where each class occupies a defined range, regardless of the number of data points per class, providing a solution to the class imbalance issue. On the other hand, when applying deep learning networks to facial forgery detection, excessively fine-grained classification criteria can lead to overfitting. This overfitting occurs because the model becomes overly focused on existing forgery techniques, resulting in limited capability to detect new types of forgeries. To address this, the proposed method employs a binary classification approach for deep learning networks. Specifically, the model is trained to classify data into two categories: genuine and forged.