zlacker

People training newer models just have to look for the "Imagen" tag or the Dall-E2 rainbow at the corner and heuristically exclude images having these. This is trivial.

Unless you assume there are bad actors who will crop out the tags. Not many people now have access to Dall-E2 or will have access to Imagen.

As someone working in Vision, I am also thinking about whether to include such images deliberately. Using image augmentation techniques is ubiquitous in the field. Thus we introduce many examples for training the model that are not in the distribution over input images. They improve model generality by huge margins. Whether generated images improve generality of future models is a thing to try.

Damn I just got an idea for a paper writing this comment.

>>rg111+(OP)
In my melody generation system I'm already including melodies that I've judged as "good" (https://www.youtube.com/playlist?list=PLoCzMRqh5SkFwkumE578Y...) in the updated training set. Since the number of catchy melodies that have been created by humans is much, much lower than the number of pretty images, it makes a significant difference. But I'd expect that including AI-generated images without human quality judgement scores in the training set won't be any better than other augmentation techniques.

>>rg111+(OP)
> Unless you assume there are bad actors who will crop out the tags.

I don't know why people do that but lots of randoms on the internet do that and they're not even bad actors per se. The removed signatures from art posted online became a kind of a meme itself. Especially when comic strips are reposted on Reddit. So yeah, we'll see lots of them.

>>rg111+(OP)
Most images you see from these services will not have a watermark on them. Cropping is trivial.

>>SirHou+Rc
Perhaps a watermark should be embedded in a subtle way across the whole image. What is the word? "Steganography" is designed to solve a different problem and I don't think it survives recompression etc. Is there a way to create weakly secure watermarks that are invisible to naked eye, spread across the whole image, resistant to scaling and lossless compression (to a point)?

>>ithkui+Vk
Invisible, robust watermarks had a lot of attention in research from the late 90s to the early 10s, and apparently some resurgence with the availability of cheap GPU power.

Naturally there's a python library [1] with some algorithms that are resistant to lossy compression, cropping, brightness changes, etc. Scaling seems to be a weakness though.

1: https://pypi.org/project/invisible-watermark/

>>SirHou+Rc
It's ironic, seeing people who build models trained on other people's work (which is in no way credited) to be worried about origin and credit.