# Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis
## 2 Related Work
### Deepfake Detection Methods  
The detection of fake images, particularly those generated by Generative Adversarial Networks (GANs), relies on identifying synthetic signatures left in the images. While visible artifacts can often be detected by the human eye, invisible markers necessitate specialized forensic techniques. Various methods have emerged in this field, including assessments of human visual performance, GAN fingerprint analysis, spatial residuals, anomaly detection, frequency space examination, and co-occurrence matrix analysis.  
A comprehensive literature review reveals that deepfake detection methods are more prevalent than creation methods, with studies indicating 11,111 detection-related publications compared to 9752 on creation [24].  
Recent research indicates a significant reliance on deep learning for synthetic media detection, with studies showing that 70% of detection methods utilize deep learning, 20% employ traditional machine learning, and the remaining 10% focus on statistical learning [25]. Within deep learning approaches, convolutional neural networks (CNNs) dominate the landscape, comprising 78% of studies, followed by recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) at 12%, with only 2% investigating region-based CNNs [26; 27; 28]. Machine learning methods, such as Gaussian Mixture Models (GMMs) and Support Vector Machines (SVMs), are also employed to enhance detection accuracy. In this study, we focus on detection of fake images of human faces. The creation of facial deepfakes is categorized under four main headings: whole face synthesis, identity modification, expression alteration, and attribute manipulation [29]. A database that allows for ongoing access to updated classification and detection strategies is actively maintained at \"Awesome Face Forgery Generation and Detection\" [30; 31]. Notable GAN models involved in face synthesis include DCGAN, WGAN, PGGAN, and StyleGAN.  
Detection methods specifically aimed at identifying GAN-generated faces can be categorized into four groups: deep learning-based approaches, feature-based identifications, physiological feature analysis, and assessments of human visual performance [3; 32]. Research has explored the detection of deepfakes based on physiological features, such as eye color and iris shape, using images generated by StyleGAN [33; 34]. Additionally, studies have shown that facial line defects can be distinguishing features between real and synthetic images, particularly in children, who tend to exhibit smoother facial transitions [35].  
Earlier studies focusing on physical attributes highlighted various detection techniques, including camera parameter analysis, interpolation effects, compression artifacts, and visual inconsistencies in eye reflections [36]. These foundational studies paved the way for more recent research that validates these detection methods for StyleGAN while noting that StyleGAN2 has effectively addressed many of the inconsistencies and artifacts identified in previous models [37].  
A significant challenge in deepfake detection is the variability in effectiveness across different GAN models. The generalization of detection methods can be compromised if images are generated by models that differ from those used in the training phase. This inconsistency may arise from the specific training datasets employed during the learning phase of the GAN.  
Overall, deepfake detection remains a rapidly advancing area of research, driven by the increasing sophistication of generative models and the need for robust verification methods.