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Comment by rogers18445 | Hacker News Reader

> It's an endless cat-and-mouse game.

This is often stated but I think it has to be obviously wrong. This isn't a traditional interactive game such as malware & anti-malware.

You have existing sensors which operate under the constraint of [ real world -> theoretic pixel space -> optics & aberrations & sensor noise -> compression ]. And a single adversary which attempts to fake this chain.

The detection of fakes isn't even an adversary in this game, it's merely a detection of deviation of the faking process.

At some point, probably soon, the faking process will reach a point where any deviation will be drowned out by the noise aspect of optics & sensors & compression.

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One method of generating things via neural networks is called a generative adversarial network. It works by having two models. One that generates content and one that detects fake content. You train them both in parallel. As the fake detector gets better, so does the generative model at generating fakes. It’s literally a cat-and-mouse game. If someone came up with a scheme to reliably detect your fakes, you could add it to your discriminator model and retrain the generator to improve the fake generation.

My understanding is that it's not quite that simple. GANs have stability problems (and as a result somewhat out of favor atm) and if the fake detection mechanism isn't a differentiable function itself no training can happen.

The fake detection mechanism (aka discriminator) is usually just another neural network and I bet that's the case here as well. So it must be differentiable and thus, if anyone ever gets a hold of it, it could be easily used to train a generator that will eventually fool the discriminator.

So, basically a NOT operator on a neural network. Does this even require a differentiation?

A NN is trained to make something indistinguishable from reality as possible - so it can't tell the difference. The inverse of that will just claim reality is fake too.

What you need a a deep fake and a real video of the same thing, then train on the difference. Clearly this is impossible - which is what makes the problem hard.

You actually don't need that. You only need a set of real videos and a generator for fake ones. Then train the discriminator to tell these two classes apart and make use of its differentiability to update the generator in tandem with the discriminator.

Would you run into class balance issues if you do not have a growing pool of initial videos?

Not if you have a balanced dataset. If not, you're already getting into trouble with GANs, which is why they were superseded by diffusers on image generation tasks.

Not really a not operator. It’s more like tuning the generated models until the fake detector cannot detect it.

It depends on how you set it up. You can use a very non-expressive valuation that assigns a score, but then you need to use reinforcement learning techniques to transform that score to a model update. Alternatively, if your valuation represents a differentiable metric function from your target you have a way of going directly from your output to a model update.

The second way requires dramatically fewer update steps (usually) than the first. Thus - having your adversarial target be differentiable definitely helps, though it is possible to do even absent such a criterion.

It is accurate that GANs have stability problems, but they are absolutely being used today for solving problems similar to this (an output needs to be "improved").

Stable Diffusion produces faces- especially eyes that are malformed. You'll often see "restore faces" in online services which feeds the end result into a GFPGAN which is used to restore faces.

Diffusors are not GANs.

indeed. that is why you use GAN to fix the face generated https://www.reddit.com/r/StableDiffusion/comments/x33rs4/how...

While Gradient Descent needs differentiable functions, there are evolutionary algorithms that do not need this and can also train neural networks.

There's an excellent sci-fi exploration of the concept and how humans interact with it in Neal Stephensons "Jipi and the Paranoid Chip"; well worth the read.

Thank you for posting this!

Link: http://readfrom.net/neal-stephenson/612819-jipi_and_the_para...

This link gave me a fake "this phone has been hacked" alert. I recommend avoiding.

Hot take: What if we just accepted that any video might be fake, just like any photograph might be fake?

And that we accept that the only thing that's assured to be real is face-to-face, and live with that reality?

It's already the legal standard in many places that a court doesn't just accept evidence like a photo or a video. Typically the photographer testifies under penalty of perjury that they were there and can be questioned about the circumstances in which they took the picture/video and any post-processing that might distort what we see.

So we can say that either it's real or we can identify the specific people who might be lying.

Unfortunately, in media and the public discourse / memosphere, that's a standard notoriously difficult to establish or enforce.

People, and crowds, will respond to first impressions.

What if we put out enough deepfakes that they get desensitized to it and just stop believing stuff on screens altogether?

Past experience suggests that that likely won't be too effective. "Big lie" propaganda is profoundly effective, even amongst those who are painfully aware of its existence and methods as individuals, and at the population level, there seem to be virtually no defences.

Rather, there'll need to be the emergence of credible and trustworthy channels which verify messaging and content before it's widely disseminated.

That largely means re-implementing the sort of media gatekeepers we've seen in the past. Though the challenge of bad-faith actors emerging in such roles at considerable scale points to further challenges, even with that model.

Under a regime in which free speech is considered a fundamental right, any sort of preemptive management by government mandate becomes intensely difficult.

>"Big lie" propaganda is profoundly effective,

I mean, people still believe we put men on the moon, and people still believe the Earth is round! /s

Some even subscribe to the belief that water is wet.

For now. It might become more important.

Don't think of a white elephant.

Don't breathe consciously.

Don't lose the game.

But if you lack technology to detect fakes how can you ever prove perjury? Photographer would then risk nothing by lying.

If the person is a professional, lying is a huge risk, as in the end of a career even if not convicted.

Earlier this year, a man who was repeatedly caught lying as a journalist was forced to resign.

Unfortunately, he was the British Prime Minister by that point.

Corroborating evidence

My startup was founded on technology to authenticate and provide provenance for digital media content.

The overwhelming response from the market, investors, etc was “We like what you’re doing and this is all well and good but the ship has sailed”.

I think the real hot take here (and they danced around this one) is essentially the partisanship. For almost everywhere this really matters a good chunk of people are more likely to believe as real whatever aligns with their pre-conceived notions, political leanings, whatever.

Essentially, real is whatever my tribe says is real.

Your startup is probably to early. As in, at some point it might be a necessity. And it’s been studied that we believe something first, and then apply reasoning to it. We skew towards our preferred bias by belief.

Thanks, I appreciate that but the experience was eye opening. Speaking of which…

Get on Facebook and watch people swap around things that are so blatantly and obviously fake. In the words of one potential investor: “no one cares”. Or, to repeat a phrase you may have heard “fake news”.

“Falsehood flies, and the Truth comes limping after it” - Jonathan Swift, 1710

Investors are dumb. See the list of people who have ftx truckloads of money. Sometimes what’s needed is a couple good sentences with create the appropriate spark in their imagination. If you can sell truth as a service, you have potentially a total addressable market of half the planet’s population.

On things like facebook usually our “guard” is down. We just assume at some point that if someone feeds it to us, it’s vetted already.

Usually a lot of context is missing with for instance a scary headline. In that sense context tools could also be great to have. Something that exposes connections between information.

I don’t believe no one cares tbh, but there are those who are resistant to change in bias.

or, conversely, to judge ideas based on their merit and not based on who says them, maybe in some deep wonderfull future ...

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