Exploration of Metrics and Datasets to Assess the Fidelity of Images Generated by Generative Adversarial Networks

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 cyberbullying. 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.