# Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis
## 1 Introduction  
Technological advancements in the past decade have accelerated the rate and quality of synthetic media being produced. This technology has various beneficial applications in diverse fields. In medical diagnostics [1], applications have been used to detect lung changes from COVID-19, and for brain tumor detection by SeGAN. Heavy industrial applications of GANs include flaw detection in materials, with studies on various surfaces using DCGAN and WGAN. Visual synthesis is another significant area, with models like Stack GAN being used for generating images from text and Discovery GAN for style transfer. Synthetic media have also rapidly been adopted for creative expression. Recent studies have employed GANs for restoring damaged artworks, achieving success in reconstructing less-damaged mosaics. As with any revolutionary technology, the negative implications of synthetic media are significant, posing risks such as violations of individual rights, the spread of misinformation, and digital fraud. This paper investigates the detectability of visual forgery. In particular, it focuses on determining authenticity of static image content generated by Generative Adversarial Networks (GANs), with emphasis on residual artifacts that elude human perception but can be detected through digital analysis. GAN models [2] excel at producing hyper-realistic synthetic images, popularly known as \"deepfakes\", due to their architecture, which continually improves the Generator neural network until the Discriminator neural network can no longer differentiate between generated and real data [3]. Recent advancements in training techniques and data quality make it increasingly difficult to distinguish GAN-generated images from authentic ones. Despite these difficulties, the literature indicates that static images generated by GANs retain digital imprints of their origins, which can assist in detection. However, detection of GAN deepfakes remains a challenging problem due to the following reasons: (i) These digital artifacts, also known as residues or residuals, are created during the learning phase across various layers of the GANs, which makes these artifacts challenging to identify. In particular, differences in training and testing conditions make it difficult to apply detection methods in real-world scenarios. (ii) While many existing studies concentrate on using datasets from known Generative Adversarial Networks (GANs), identifying high-resolution deepfakes generated by unseen models remains particularly challenging. (iii) The literature lacks a systematic classification and study of residues. (iv) The implementation of noise-canceling methods [4] and residue removal methods [5; 6; 7] in advanced GAN models significantly hampers detectability. Studies have shown that specific GAN models, like PGGAN, can produce detectable artifacts, while others, like STGAN, do not synthesize noise values, complicating detection. In this research, we present a GAN simulation capable of detecting artifacts and deepfakes by identifying common features across different applications that examine GAN-generated artifacts. This simulation offers a multi-faceted method for detecting artifacts from specific GAN models, with results shared and analyzed for detection and classification performance in the context of deepfake identification. This research introduces four significant advancements:  
1. Proposed Taxonomy: A classification framework for the study of artifacts in GAN deepfake detection, particularly in facial image deepfakes, is proposed. A major problem this paper addresses is the absence of classification studies defining GAN residues. By analyzing GAN residues, this study contributes to a deeper understanding of detection methods and presents a taxonomy for artifact detection.