Everyone in the ML field understands that ML-assisted upscaling doesn't produce output that accurately represents the original full-resolution image. It produces output that humans perceive to be realistic-looking, or free of traditional upscaling artifacts.

While this is obvious to anyone familiar with the technology, it's difficult to explain to casual observers. The image output looks real. It feels like it could be real. It's free of traditional scaling artifacts that would trigger suspicion. Without additional explanation, it's easy to see why casual observers would assume the hallucinated upscaled version is an accurate representation of the original image.

Historically, police sketches and blurry surveillance images are obviously low quality enough that people inherently know they're approximations. The problem with these ML hallucinated upscaled images is that they look and feel real enough that they bypass people's suspicions. We can try to present them as "Here's what the suspect might look like", but when they look like a full-resolution photograph, people will simply assume that it's exactly what the suspect looks like.