Ah, this old paper again. When it first came out it got raked over the coals pretty thoroughly. The authors used an older, poorly-trained version of Stable Diffusion that had been trained on only 160 million images and identified 350,000 images from the training set that had many duplicates and therefore could potentially be overfitted. They then generated 175 million images using tags commonly associated with those duplicate images.

After all that, they found 109 images in the output that looked like fuzzy versions of the input images. This is hardly a triumph of plagiarism.

As for the watermark, look closely at it. The AI clearly just replicated the idea of a Getty-like watermark, it's barely legible. What else would you expect when you train an AI on millions of images that contain a common feature, though? It's like any other common object - it thinks photographs often just naturally have a grey rectangle with those white squiggles in it, and so it tries putting them in there when it generates photographs.

These are extreme stretches and they get dredged up every time by AI opponents. Training techniques have been refined over time to reduce overfitting (since what's the point in spending enormous amounts of GPU power to produce a badly-artefacted copy of an image you already have?) so it's little wonder there aren't any newer, better papers showing problems like these.