The research area of AI-generated image detection and de-identification is experiencing significant advancements, particularly in addressing the challenges posed by the democratization of synthetic image creation. A notable trend is the exploration of the impact of prompts on the detectability of AI-generated images, with studies indicating that more detailed prompts lead to images that are easier to detect as synthetic. This insight is crucial for developing more robust detection models and for understanding the nuances of human vs. AI detection capabilities. Additionally, there is a growing focus on the ethical and regulatory implications of deep fakes, with researchers highlighting the need for clearer definitions and transparency obligations under current legislation. In the medical domain, advancements in de-identification techniques are being made, with the introduction of novel datasets and benchmarks that ensure patient privacy while maintaining the integrity of medical data. These developments underscore the importance of integrating domain-specific knowledge into de-identification processes. Lastly, the role of face alignment in image quality assessment is being scrutinized, revealing that alignment significantly impacts quality metrics, especially under challenging conditions. This research highlights the need for more comprehensive evaluations of alignment's influence on facial analysis tasks.