# Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis
## 1 Introduction
In addition, a detection study was conducted using a unique facial image dataset crafted from three identified GAN models, allowing for a comparative analysis of model performance and assessing the model's ability to recognize images produced by other unseen models. 2. Development of a Unique Multi-GAN Dataset: Unlike many studies that focus on single known GANs, our approach combines three different GAN models, StyleGAN3, ProGAN and InterfaceGAN [8; 9; 10]. These GAN models were particularly selected for their popularity and success in the generation of deepfake face images. We created a mixed dataset to aid researchers in exploring GAN detection, particularly for facial deepfakes, addressing the challenges associated with the absence of diverse datasets in the field. Additionally, our specialized mixed dataset for GAN detection will serve as a valuable resource for researchers, facilitating studies involving mixed datasets generated by various GAN models--a notable gap in the current landscape. 3. Combining Frequency Space and RGB Color Correlation Methods of Artifact Detection: In particular, we analyze GAN definitions through frequency and generalization analyses of the identified residues and their repetition across various GAN models. By examining frequency space analyses and integrating them with additional residue detection methods, we aim to enhance the accuracy and performance of detection techniques in this field. 4. Our novel method achieves an accuracy of at least 87% as seen in Section 6, outperforming current methods such as the RealForensics detector [11], which has a state-of-the-art generalization performance on unseen datasets with a high accuracy of 83% and 75% for real and fake images. In addition, our proposed method also achieves 100% accuracy when the classifier used is Support Vector Machine with RBF kernel. In particular, we showcase that, contrary to previous belief [12], artifact-based synthetic face image detection can, in fact, produce extremely high accuracy, precision, recall, and F1 scores using various classifiers. The purpose of this study is to enhance detection capabilities and achieve higher accuracy in identifying GAN-generated deepfakes of facial images. In particular, we wish to address the generalization problem in deepfake detection whereby a detector does well when the training and test data are created using the same techniques, but performs poorly when deepfakes from unfamiliar sources are presented to the detector, as is bound to happen in real-world scenarios [13]. By targeting the detection of unfamiliar deepfakes, we aim to analyze digital residues through a combination of feature extraction and frequency domain transformations. We choose this approach for two reasons. (i) Speed and efficiency of resource utilization: While several deepfake detection pipelines based on deep learning exist, which can automatically extract features and classify them, we choose to preprocess our images first for feature extraction and then follow this with classification with traditional machine learning classifiers because our approach is far more economical in terms of compute power usage and time taken. Our algorithm does not need training and can be directly applied on images from never before seen models. As seen in Section 6, our algorithm runs in a fraction of seconds. (ii) Explainability: While previous deepfake detection applications have attempted image preprocessing, we have conducted a more in-depth exploration of feature extraction and adapted the features to better align with their detection capabilities when anomalous. Additionally, we addressed a critical gap in the field by introducing a multi-GAN dataset. The detection framework operates without prior knowledge of the multi-GAN datasets or their architectures; it relies solely on feature extraction and analysis to classify images as either authentic or synthetic, enabling further advancements in the field.