DragGAN consists of two main components including: 1) a feature-based motion supervision that drives the handle point to move towards the target position, and 2) a new point tracking approach that leverages the discriminative GAN features to keep localizing the position of the handle points.

As a technology this is tremendously impressive, straight out of SF movie. However I wonder how it will impact our culture, fashion, standards of beauty etc. when more and more artists will be accepting the generated output then creating their own. Just like in case of music the invention of MIDI, synths and sequencers brought new styles but also boring and imagination-numbing standardization.

Neat concept. I wonder if something like that can be applied to diffusion models (the new kid on the block that is outshining GANs right now) - especially since the technique doesn't seem to be too dependent on the generative image implementation.

Also, it's interesting that they're submitting to SIGGRAPH - kind of expected this to be in a more ML-ish conference.

Probably goes to show where SIGGRAPH is headed.

Hello, post-truth world!

More seriously, I think that digital photos, and especially low-res surveillance camera coverage, will soon be inadmissible in any reasonable court, because tools like this would allow to forge such evidence in very natural-looking ways.

Seeing how slowly laws move, the cynic says it's: should be inadmissible in any reasonable court, but will continue to be admissible, and will take a major set of incidents for changes to be enacted across many countries.

Yes. The story if admissable DNA evidence is instructive and terrifying.

https://daily.jstor.org/forensic-dna-evidence-can-lead-wrong...

There are probably ways to embed cryptographic hashes within images. Any device that creates images from the real world could have secret keys that can be used to validate any image created by said device.

We will still need a centralized party that holds the secret keys for validation through

This is the right approach, but there’s lots of complexity around transcoding. In the courtroom that’s less of an issue if you can get the original unmodified outputs, but broader applications need to think through what it means to be “verified”

What about the recording device itself? Refeed a video/frame/hash back to a security camera and it tells you if it was originally sourced from that specific camera or not.

The sci-fi dystopian answer would be entangled photon lights and off site image recording that preserves entanglement :-)

What shall we do with millions upon millions of existing mobile phones, and also surveillance cameras and dashcams?

Some of them possibly could be updated, but this will take time. Securing the keys within them is also going to be a problem; not all of them have a TPM.

There's no way this would work, either the master encryption key would be leaked, or someone would reverse-engineer the chip.

Also, what about someone putting a screen just in front of the sensor?

No need for that.

A private key is generated on device and never leaves it. It sits inside a TPM or equivalent.

The public key is pushed to a well-known site, visible to all.

Every shot is signed by hashing the bits into a reasonably short string (say, using sha512) and then encrypted with the private key.

Anyone can now decrypt the hash, and compare it with the hash they computed from the bits.

The problem, of course, is that any transformation whatsoever breaks the signature. You can't adjust levels and contrast, you can't even crop. Maybe it's a good property.

Why would you trust the key if it was generated on the device? Anyone could generate a key. You would either need a centralized server to sign that key, in which case you have to trust a centralized company / government agency.

You don't need a centralized database. You have 3 companies 3 different hashes. They catch bob stealing, on 3 different cameras. Each footage can be independently verified as authentic and not doctored. This would prevent anonymous found footage suggesting someone committed a crime.

You definitely don't want one single leakable entity.

Being able to fake something really doesn't matter all that much, as you'd still need to get that fake video into the surveillance camera system and you need to do so in the time between committing the crime and the police arriving, and without leaving a trace and hoping that whoever you try to incriminate doesn't have an alibi.

Fakes will be relevant for Twitter, TikTok and Co., where random videos are posted and distributed without sources, heavily edited and compressed, such that it is impossible to tell if that video ever started out as a real video or a fake. But in court the whole thing starts to fall apart the moment they ask where that video came from.

Without knowing what the current ones are, I'd guess we need improved chain-of-custody laws for evidence immediately!

Ah, but for law enforcement types to say "hey, look what we found on the surveillance video server!" is a potential problem. The power asymmetry in society is ripe for exploitation by the powerful. They just need some goons - of which there are plenty - to do the dirty work.

You have always been able to forge photographic evidence in natural looking ways (most easily by using completely real photography with misrepresented time or context).

And, of course, the easiest to falsify evidence (eyewitness testimony) is still admissible.

That's why you have to provide support for provenance, all of which is subject to examination and counterevidence.

Mirror https://web.archive.org/web/20230519060439/https://vcai.mpi-...

GitHub (no code yet, only demo GIF) https://github.com/XingangPan/DragGAN

arXiv https://arxiv.org/abs/2305.10973

I'm thinking that soon video and images are going to be just dead weight in journalism, adding nothing other than decoration.

Stock photos are already like that. To me they're worse than having no pictures at all. Generated images are better than nothing because you can make them very specific.

I think there has to be some kind of cryptographic signature solution, like "The BBC verifies that this image is authentic".

We already have most of the required cryptographic primitives for this. PKI, trusted timestamping, secure hardware with remote attestation are some of the necessary building blocks. All that's really missing are camera sensors with built-in cryptography.

And societal care. Our society seems to really like to whine about "the danger of deepfake images", but our actions reveal that we don't really give a crap, as we could solve this problem today if we really wanted to.

I think it would also need to have some kind of embedded version control from camera through Photoshop so you know what was changed.

If you trust how the bbc tells the story, you also trust them when they say the image is true. Not a technical problem IMO.

Sure but then the image gets copied shared reposted etc.

But why? If it is on bbc s website bbc is already saying it is good. Same for any other media. The technical approach is interesting but not needed IMO in this case.

Like I said, because you might come across the image on Reddit or Twitter but have no idea if it's real or not.

It seems to be a pretty common thing now on news network websites, that at the top of the story, or perhaps somewhere in the middle, there is a video. But, you click on the video, and it's something entirely unrelated to the article. I feel like this as been going on for quite a while, nothing to do with synthetic media, but just an observation of an annoying pattern I've noticed.

Why just the video and images? Half of the stories on the Internet are created out of whole cloth by AI now, never mind the embedded media...

The only thing that's new is the interactive interface the rest of it is old tech... You can use it on art breeder.com. Photoshop even has it in their face neural filter. GANs are not feasible for a variety of reasons you need to have models specific to your subject ie. why they change to a elephant model to manipulate the elephant.they are also not style agnostic but it's a great demo and the right time to release it. Just before the summit of the hype curve I bet one VC is dumb enough to throw millions at them.

With ChatGPT, the only thing new was the chat interface. In fact even Sam Altman mentioned this on Lex Fridman's podcast, IIRC - he said what he was most surprised about was the outsized effect the interface had on bringing LLM to the forefront of public consciousness, despite the existing maturity of the underlying GPT models. At least in that case it was OpenAI adding interactivity to its own existing models. But similarly, from a more holistic viewpoint, OpenAI productized existing research from Google. Transformer models were "old tech" since Google published "Attention is all you need" in 2017... and yet, when OpenAI managed to turn it into a usable product, suddenly they became the first movers and the company to beat. So I'm not convinced that only a "dumb" investor would fund an effort with a proven ability to productize "old tech."

The I don't understand the technology but will ramble about stuff until they just give up reading the comment approach ¯\_(ツ)_/¯

Are you referring to my comment? I'm certainly no expert on AI, and if I'm misunderstanding the technology I'd like to know. What is wrong about what I wrote?

Yes, I am referring to your comment and I am not going to explain why everyone and their mother jumped ship from GANs and went all in on transformers. Well, there is still the Alan Turing Institute in the UK but even they gave up and are into NFTs now :D

This reads a lot like 'dropbox is trivial, rsync already exists'

Best demo would be to take some famous photo, find its position in gan-space then move it around.