On the use of Stable Diffusion for creating realistic faces: from generation to detection

The mass adoption of diffusion models has shown that artificial intelligence (AI) systems can be used to easily generate realistic images. The spread of these technologies paves the way to previously unimaginable creative uses while also raising the possibility of malicious applications. In this work, we propose a critical analysis of the overall pipeline, i.e., from creating realistic human faces with Stable Diffusion v1.5 [1] to recognizing fake ones. We first propose an analysis of the prompts that allow the generation of extremely realistic faces with a human-in-the-loop approach. Our objective is to identify the text prompts that drive the image generation process to obtain realistic photos that resemble everyday portraits captured with any camera. Next, we study how complex it is to recognize these fake contents for both AI-based models and non-expert humans. We conclude that similar to other deepzfake creation techniques, despite some limitations in generalization across different datasets, it is possible to use AI to recognize these contents more accurately than non-expert humans would.