###### Abstract  
Advancements in technology have improved human well-being but also enabled new avenues for criminal activities, including digital exploits like deep fakes, online fraud, and cyber-bullying. Detecting and preventing such activities, especially for law enforcement agencies needing photo profiles for covert operations, is imperative. Yet, conventional methods relying on authentic images are hindered by data protection laws. To address this, alternatives like generative adversarial networks, stable diffusion, and pixel recurrent neural networks can generate synthetic images. However, evaluating synthetic image quality is complex due to the varied techniques. Metrics are crucial, offering objective measures to compare techniques and identify areas for enhancement. This article underscores metrics' significance in evaluating synthetic images produced by generative adversarial networks. By analyzing metrics and datasets used, researchers can comprehend the strengths, weaknesses, and areas for further research on generative adversarial networks. The article ultimately enhances image generation precision and control by detailing dataset preprocessing and quality metrics for synthetic images. g _applied_ sciences  
13 July  
Of the aforementioned models, this research will only focus on GANs for their visual quality, as they can generate high-quality, realistic images that are often difficult to distinguish from actual images. Additionally, GANs need to strike a balance between the intensive use of computational resources and the speed with which they can generate synthetic images. This versatile technology generates lifelike images, videos, and audio, applicable to diverse fields like entertainment, medicine, and drug discovery. Successful GANs hinge on a distinct discriminator differentiation between real and generated data, achieved through specific distance metrics in loss functions. Instabilities in GAN training can be mitigated by altering the loss calculation's distance metric [5]. GANs have demonstrated their versatility in generating various types of data, including images, videos, and text, making them applicable in diverse domains such as computer vision, natural language processing, and generative modeling. With advancements in artificial intelligence technology in image processing, GANs have emerged as a powerful tool for generating high-quality synthetic images through deep learning techniques [6]. By leveraging GANs, which belong to unsupervised learning algorithms, it has become increasingly challenging for human observers to visually distinguish between real and fake images generated by these models. The realism achieved by GAN-generated images has reached a remarkable level, making it difficult for the naked eye to discern any differences between the real and synthetic counterparts. GANs represent a nascent field of study with considerable untapped potential in unexplored realms of application. As the adoption of GANs proliferates, we can anticipate the emergence of increasingly inventive and innovative utilization scenarios. The authors in [7] explain that facial privacy protection aims to remove sensitive information from images to protect privacy. Early methods used masking, blurring, or pixelation, but these often made the faces undetectable and unsuitable for standard computer vision pipelines. Later studies used GANs to generate synthetic images as a solution for facial de-identification. The motivation for this study lies in the need to establish a quality standard in the use of technologies related to the generation of synthetic images in various fields. These technologies often generate synthetic images with defects, which limits their usefulness and the full potential they could offer. This situation points to a gap between the theoretical capabilities of these technologies and their practical application. The rest of the work is organized as follows: Section 2 describes what a GAN is, how it works, and its variants.