# Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis
## 8 Conclusions  
The problem of detecting fake images is becoming increasingly harder due to the increased sophistication of the methods used to produce them, especially using Generative Adversarial Networks (GANs). In this study, we have made three important contributions to tackle this challenge. Covering three different transform methods, three GAN models, and twelve classification methods, ours is the most comprehensive study of detection of static deepfake face images produced by GANs. First, we have identified and classified the various types of GAN artifacts that can help us successfully differentiate fake images from real ones. A comprehensive taxonomy categorizes these residues into visible and invisible types, with invisible residues further divided into four categories: GAN generalization artifacts, GAN fingerprints, spatial domain features, and frequency space features. In this study, we specifically explored frequency spectrum artifacts, a subset of invisible artifacts that can only be detected through digital analysis methods. We have developed feature extraction methods; after applying these, we were able to use simple machine learning classifiers to detect deepfakes with great accuracy extremely fast. Second, we have developed a mixed dataset of fake facial images, incorporating StyleGAN3, ProGAN, and InterfaceGAN, with over 15,000 images. This specialized dataset for GAN detection will support research on mixed datasets generated by different GAN models, addressing a significant gap in the current field. Third, we have developed a novel deepfake detection pipeline that analyzes digital residues in images through a combination of RGB color analysis and frequency domain transformations such as Fourier, wavelet, or HOG transforms. Using these novel feature extraction methods we achieved impressive fake image detection performance across various classification models. When evaluating newly supplied images or datasets, applying the proposed method, which encompasses all data preparation methods and classification techniques, consistently yields results. To ensure meaningful comparisons and accurate benchmarking, incorporating authentic real images is essential to validate the integrity of the analysis. Our results demonstrate significant promise in deepfake face image detection, with outstanding performance in accuracy, recall, precision, and F1 scores across various classifier algorithms, even achieving 100% accuracy in some cases. Naive Bayes, the fastest algorithm at 0.03 s, achieves at least 88% accuracy. Among the datasets, we find that InterfaceGAN fakes are easier to detect than those generated by ProGAN and StyleGAN3. When using HOG and wavelet transforms, StyleGAN3 fakes are the most challenging to detect. The Gaussian Mixture Model, however, proves to be less effective as a classifier, achieving under 20% accuracy on the StyleGAN3 dataset. Among gradient boosting techniques, XGBoost delivers the best and most time-efficient results compared to Gradient Boost and AdaBoost. Neural networks consistently perform excellently across datasets and transforms, though they lack interpretability. The encouraging results of our work highlight several promising avenues for future research. While we have tested our detection pipeline with three GAN models, our architecture can be further tested with a variety of other models and datasets. Additionally, other image preprocessing techniques, either individually or in combination, could be investigated to improve the efficiency of classification results. Another key area for exploration is the explainability of the features extracted in our detection pipeline, which could provide deeper insights into how fake images are identified. Other state-of-the-art detection methods presented in the taxonomy could also be integrated with our framework to further enhance the accuracy of fake image detection. Conceptualization, T.S. and M.A. ; methodology, T.S. ; software, T.S.