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How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

nytimes.com

31–40 of 83 posts

Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#31
post #29
post #5

Scary to think where this will be in 10 years. Perhaps even video evidence will be hard to believe any more. How do you convict someone if this technology is mature?

Heinlein's fair witness?

This side of the house is white.

Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#33
post #12

Good article and great tech. However, I don't know if I believe the results are as good as they claim. Many of the pictures look a bit off to me, like they all have dead eyes. Maybe celebrities generally look like that anyway, so it is being true to form. :) In particular, I think this guy is missing a pretty significant part of his head: https://static01.nyt.com/newsgraphics/2017/12/26/ai-faces/8e...

The hair was the giveaway to me. I stared at the two "which one is real" images for a couple minutes thinking, "They both have that fake looking wavy hair, I thought for sure neither was real."

Cheap trick NYTimes. Cheap.

Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#34
post #12

Good article and great tech. However, I don't know if I believe the results are as good as they claim. Many of the pictures look a bit off to me, like they all have dead eyes. Maybe celebrities generally look like that anyway, so it is being true to form. :) In particular, I think this guy is missing a pretty significant part of his head: https://static01.nyt.com/newsgraphics/2017/12/26/ai-faces/8e...

Even better: https://static01.nyt.com/newsgraphics/2017/12/26/ai-faces/8e...

That's one heck of a receding hairline, meaning receding out of the plane of existence.

Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#36
post #29

Earlier quoted context omitted.

Heinlein's fair witness?

This side of the house is white.

You cannot infer that the type of structure supporting the visible surface is a house, or part of one.

Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#37

Earlier quoted context omitted.

This side of the house is white.

You cannot infer that the type of structure supporting the visible surface is a house, or part of one.

By that scheme you can not infer it is a surface either. But that was the example given in the book.

Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#38
I don't understand the images associated with that article. They purport to show the progressive refinement of the output over a series of days. But the figure changes dramatically from image to image, all the way to the end of the run.

At the very least it seems the output is not stable: a human has to decide when to stop the Wheel of Fortune. It looks more like a series of images taken from different training sets or parameters, for the NNs I'm used to.

Caveat: I've done a lot of ML, but not GANs specifically. Is this common? How do you solve the 'where to stop' problem if the output is so unstable?

Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#39
post #21
post #12

Good article and great tech. However, I don't know if I believe the results are as good as they claim. Many of the pictures look a bit off to me, like they all have dead eyes. Maybe celebrities generally look like that anyway, so it is being true to form. :) In particular, I think this guy is missing a pretty significant part of his head: https://static01.nyt.com/newsgraphics/2017/12/26/ai-faces/8e...

The inventor of GANs, which have been considered the most interesting idea in ML in the last decade, is Ian Goodfellow. I met him on reddit a few years ago. I was supposed to get private ML tutoring from him, just around the time Andrew Ng opened the first Coursera course. I didn't get lessons because I gave up and eventually took the MOOC. But it's amazing to know we share the same forums and sometimes exchange a co…

> The most famous problem of GANs is instability during training and mode collapse - which is like a student learning especially for an exam (and not in general) thus optimising for the test instead of the real thing.

I must confess I haven't worker with GANs yet, but isn't that the whole point of GANs? Student is optimising for the test while the teacher is learning how to make tests as similar to reality as possible?

If I understand correctly, the main challenge is finding a way to allow teacher and student (well, generator and adversary) to learn at a similar rate, so that one doesn't stop learning because its competitor is too advanced. Is that correct?

Re: How an A.I. ‘Cat-And-Mouse Game’ Generates Believable Fake Photos

#40
post #17

Earlier quoted context omitted.

Yeah, I thought both looked a tiny bit off. I think it has to do with the reflection in the eyes which is a tiny bit inconsistent, among other things.

maybe so (they fooled me) but you were already prepped to scrutinize them. To the point others have made, we’ll soon need to be constantly prepared to assume fakery. the technology of fakery is rising the meet the “everything is fake news” moment

I immediately picked the right image, because I saw whisker stubble on the left, and I already knew that image-generation AIs seem to have a thing for painting whisker stubble all over anything even remotely resembling a male face.

Surprise! Guess I should have considered the possibility of a trick question.

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