# Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis
## 4 Methodology of the Novel Deepfake Detection Framework
### Classification Techniques  
Once the image data are preprocessed and features are extracted and scaled, the images are ready for classification. Classifiers vary in complexity and assumptions about the data, affecting their suitability for different tasks. Simple models like Logistic Regression and k-Nearest Neighbors (k-NNs) are easy to interpret but have limitations, such as assuming linear relationships and being computationally expensive with larger datasets. k-NNs is also sensitive to feature scaling. Naive Bayes is fast and effective but assumes feature independence, a condition that may not always hold true in practice. Random Forests and Extra Trees are valuable for understanding feature importance, especially with controlled tree depth. Support Vector Machines (SVMs) excel at handling high-dimensional data, while Gaussian Mixture Models are great for managing complex data distributions with clustering and overlaps. Neural networks are powerful but computationally demanding and less interpretable. Lastly, XGBoost, Gradient Boosting, and AdaBoost offer high accuracy but require careful tuning. Below is a brief description of the classifiers used in our experiments. * The **Gaussian Mixture Model (GMM)** is a probabilistic model that classifies data as a mixture of Gaussian distributions. It treats real and fake images as mixtures based on features like color channel differences and frequency domain patterns. GMM performs soft classification, estimating the likelihood that an image belongs to a class. This helps to handle uncertainty and overlap between the two classes, thus being especially good for detecting realistic fakes. It is especially effective for identifying frequency domain anomalies. Training uses the Expectation-Maximization (EM) algorithm to optimize the model's parameters to maximize the likelihood of the observed data [50]. * **Gradient Boosting, XGBoost, and ADA Boost** are ensemble methods that combine weak learners, usually decision trees, to improve predictions. Gradient Boosting builds trees sequentially, correcting errors using the gradient of the loss function. AdaBoost focuses on misclassified instances, giving them higher weights. XGBoost enhances Gradient Boosting with regularization to reduce overfitting and improve speed, handling sparse data and missing values effectively. It excels with structured data and complex non-linear relationships. * **Random Forest** is an ensemble method that builds multiple decision trees using random subsets of data and features, enhancing robustness. By adjusting tree depth, it prevents overfitting, focusing on key features. It also offers interpretability through feature importance metrics, helping identify influential features. Random Forest handles high -dimensional data, such as large feature sets from frequency transformations and color-difference analyses, effectively [59]. * **Neural Networks (NNs)** with multiple dense layers automatically learn relevant features from frequency domain and color-difference metrics [34]. They capture both global patterns (e.g., face shape) and subtle local differences (e.g., blending artifacts). Neural Networks are scalable, improving performance with larger datasets and more complex features, but they lack interpretability. Our Neural Network classifier uses one input layer, two dense hidden layers with ReLU activation, and a final output layer with sigmoid activation. The binary cross-entropy loss function and cross-validation were applied. * **Support Vector Machine (SVM)** is a powerful supervised learning model for classification and regression. It finds the optimal hyperplane that maximizes the margin between support vectors. SVM handles non-linear relationships using kernel functions, which map input data to higher dimensions for separating non-linearly separable classes. Common kernels include linear, polynomial, and RBF. SVM is particularly effective in high-dimensional spaces [59]. * **K-Nearest Neighbors (KNNs)** is a simple, non-parametric algorithm for classification and regression.