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

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

#61

Post by schaefer » Thu, Jan 04, 2018, 8:44 PM UTC

saycheese wrote: post #14

>> “QUESTION: Look at the two photos below and see if you can figure out which person is real.” >> “ANSWER: Sorry! This was a trick question. Both images were generated by computers.” Not really a trick question when even if you know they’re both fake that the only way to be right (confirm you are right) is to be wrong.

I’d also like to comment on the “ha, fooled you!” tactic used in this article, where the authors asks the reader to choose the photo of the real person from two given photos and then reveals that, gasp, both are computer generated.

Whenever I run into this often-used tactic in papers and talks, I can’t help but feel – no, the author didn’t just convince me of their point. Instead they convinced me that they don’t value being trustworthy. Often I will just stop reading the article right then. Or if I do continue I will become unforgivingly skeptical of any claim that doesn’t provide a citation that is independently verifiable.

Use of the tactic feels particularly peculiar in an article which itself grasps towards the implications of a future in which photos and videos are no longer trustworthy, a future in which personal reputation will be more meaningful.

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

#62

Post by Retric » Thu, Jan 04, 2018, 8:51 PM UTC

delecti wrote: post #24

oillio wrote: 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...

You were primed to be looking for flaws by the nature of the article. It wouldn't be hard to come up with a context where each and every one of the 3x3 grid of pictures in that article was accepted at face value.

They might work as thumbnails, but these are terrible when blown up to full size. When given both images I was trying to find one that might be real thinking it could be some freaky filter or something. And I still had a 'these are terrible fakes feeling.'

Even the 'best' headline image fails as the eyes are not the same size and the rest of the face just looks off.

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

#63

Post by delecti » Thu, Jan 04, 2018, 9:01 PM UTC

Retric wrote: post #62

delecti wrote: post #24

Earlier quoted context omitted.

You were primed to be looking for flaws by the nature of the article. It wouldn't be hard to come up with a context where each and every one of the 3x3 grid of pictures in that article was accepted at face value.

They might work as thumbnails, but these are terrible when blown up to full size. When given both images I was trying to find one that might be real thinking it could be some freaky filter or something. And I still had a 'these are terrible fakes feeling.' Even the 'best' headline image fails as the eyes are not the same size and the rest of the face just looks off.

Did you even read my comment? They're not perfect, but you were expecting them to be fake. Someone not told there would be computer generated images would be considerably easier to fool.

Also, probably the bigger risk is not that you'll be shown an entirely fabricated image, but rather that someone could convincingly be inserted into an existing image.

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

#64

Post by yummybear » Thu, Jan 04, 2018, 9:38 PM UTC

oillio wrote: 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...

Several of them seem to be using many features from specific celebrities. It may just be me, but there is a very strong similarity to Paul Walker, Liv Tyler, Michael Douglas and Adam Sandler in some of these. I don't know if it's a result of overfitting?

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

#65

Post by vinn124 » Thu, Jan 04, 2018, 10:01 PM UTC

amenod wrote: post #39

visarga wrote: post #21

Earlier quoted context omitted.

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 possibl…

> but isn't that the whole point of GANs?

not quite, but youre on the right path.

think about it this way: you (the generative model) are trying to predict a unit gaussian, which is just a fancy way to say bell curve. you get +1 if you predict a number in this distribution (eg 0.1 or -0.5, which is within one standard deviation of the mean of 0); you get -1 if you predict a number thats "far" from this distribution (something like 40 - which has an infinitesimally low probability of being drawn from a unit gaussian).

mode collapse, then, is when you predict 0 all the time. yes, you are technically correct but youve failed to learn the true distribution.

obviously ive simplified this quite a bit and have anthropomorphize the model, but i hope you get the gist. otherwise, the [original paper](https://arxiv.org/abs/1406.2661) is refreshingly easy to read.

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

#66

Post by vinn124 » Thu, Jan 04, 2018, 10:06 PM UTC

ouid wrote: post #42

oillio wrote: 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 test of believability they give in the article is also bullshit. Both of the options are fakes, they both even look like fakes. Her hair and forehead doesn't make sense, his mouth and ears don't make sense. They don't match it up against a real picture because people's performance on that task would contradict the headline.

> The test of believability yep! that is the fundamental limitation of adversarial networks. theres no good measure or "loss", as it's highly subjective.

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

#67

Post by claytonjy » Thu, Jan 04, 2018, 10:11 PM UTC

visarga wrote: post #21

oillio wrote: 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…

> RL is supposed to be the way to AGI

Could you expand on that? The more I read from folks like LeCunn & Chollet seem to disagree strongly. Just this week Yan posted about unsupervised modeling (with or without DL) to be the next path forward, and described RL as essentially a roundabout way of doing supervised learning.

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

#68

Post by chuckdries » Thu, Jan 04, 2018, 10:27 PM UTC

oillio wrote: 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...

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

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

#69

Post by bitL » Thu, Jan 04, 2018, 10:45 PM UTC

visarga wrote: post #21

oillio wrote: 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…

> private ML tutoring from him

> I didn't get lessons because I gave up and eventually took the MOOC

Udacity still didn't get him onboard. I took DLF ND because of the tutoring they promised, did GANs as my first project to be in the queue, then graduated later still with no mentoring sessions. So you didn't miss anything by dropping out. How were Ng's new lessons? Worth taking it if I did DLF + fast.ai already?

BTW, GANs main use might be allowing almost fully unsupervised learning by extending small datasets with believable data.

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

#70

Post by taneq » Fri, Jan 05, 2018, 12:01 AM UTC

bitL wrote: post #69

visarga wrote: post #21

Earlier quoted context omitted.

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…

> private ML tutoring from him > I didn't get lessons because I gave up and eventually took the MOOC Udacity still didn't get him onboard. I took DLF ND because of the tutoring they promised, did GANs as my first project to be in the queue, then graduated later still with no mentoring sessions. So you didn't miss anything by dropping out. How were Ng's new lessons? Worth taking it if I did DLF + fast.ai already? BTW,…

> BTW, GANs main use might be allowing almost fully unsupervised learning by extending small datasets with believable data.

I've wondered if dreams are basically this. Your brain uses its world-model-prediction subsystem to generate plausible inputs against which to train its action-generation-policy subsystem. Then, in real life, the action-generation-policy subsystem can react much more appropriately and quickly to real events.

Also, toddlers' stream-of-consciousness babbling when they first start talking. They narrate everything and more than once I've wondered if it's essentially them generating their own verbal training data. When they start talking to themselves their pronunciation, grammar etc. start improving much more rapidly.