# Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis
## 3 Taxonomy of GAN Artifacts  
The study and classification of GAN artifacts have gained significant attention in recent years. Influential studies [38; 39] laid the groundwork for this area of research, establishing a systematic approach to understanding the artifacts produced by GANs. GAN artifacts can be broadly categorized into two types: those distinguishable by the human eye and those that are not [3]. Visible artifacts are detectable through human visual perception, whereas invisible artifacts can only be identified through digital analysis methods. Figure 2 illustrates the taxonomy of GAN artifacts. Invisible GAN artifacts are further divided into four classes:  
1. Residues from GAN Generalization: These artifacts arise when a GAN struggles to learn the distribution of the training data, often due to convergence issues between learning and testing phases. Current GAN objectives and their internal architecture do not adequately promote a wide variety of distributions in synthetic outputs [40]. While this situation may seem advantageous because the parser effectively generalizes the learning process, it also fails to recognize the lack of diversity in outputs [41]. This can prevent the generation of diverse synthetic outputs, suggesting that simply increasing the quantity and diversity of training data may not suffice. Some GANs struggle with generating realistic content when predefined patterns, like typical room layouts, are not present. These phenomena highlight the necessity for improvements in GAN architecture to enhance generalization capabilities. 2. GAN Fingerprints: Each GAN model leaves unique fingerprints on the synthetic images it generates, detectable through model-based or image-based analysis. Characteristics such as training set distribution, network architecture, hyperparameters, and optimization functions contribute to these fingerprints. Distinctive marks are consistently left by various GANs across different architectures and datasets [42], which can be represented as an RGB image of the same dimensions as the original [43], thereby validating the concept of GAN fingerprints. The study of [44] also states that the GAN fingerprint can be uniquely detected according to the GAN model and that the obtained fingerprint depends on the dataset used during training. 3. Spatial Domain Features: Artifacts in this category stem from the inherent limitations of the GAN model. For instance, GANs may struggle to create images with proper saturation or to replicate the varied exposure levels typical of real-world cameras, leading to the absence of certain photographic attributes in their outputs [45]. Other studies [46] have focused on identifying synthetic images by examining the co-occurrence of RGB pixels. Moreover, since GANs generally operate in the RGB color  
Figure 2: GAN artifacts can be broadly classified as visible and invisible. The latter can be further divided into four classes. space, detection can be performed using statistical methods that analyze chrominance values from the residues in RGB, HSV, and YCbCr color spaces [47]. 4. Frequency Spectrum Artifacts: This category of artifacts is identified through frequency space analysis, which transforms synthetic images to reveal distinctive patterns. Research has shown that Discrete Cosine Transform (DCT) methods can effectively differentiate between real and synthetic images, as well as identify the specific GAN model used for their generation [48,49]. Additionally, Fourier transforms have been employed for deepfake detection [50], and watermarking techniques in frequency space have been developed to safeguard intellectual property [51]. Understanding the taxonomy of GAN artifacts is crucial for improving the detection of synthetic images. Ongoing research is uncovering the complexities of these artifacts, offering insights that can enhance the technology's capabilities and its applications across various fields.