Launch HN: Reality Defender (YC W22) – API for Deepfake and GenAI Detection

We've actually deployed to several Tier 1 banks and large enterprises already for various use-cases (verification, fraud detection, threat intelligence, etc.). The feedback that we've gotten so far is that our technology is high accuracy and a useful signal.

In terms of how our technology works, our research team has trained multiple detection models to look for specific visual and audio artifacts that the major generative models leave behind. These artifacts aren't perceptible to the human eye / ear, but they are actually very detectable to computer vision and audio models.

Each of these expert models gets combined into an ensemble system that weighs all the individual model outputs to reach a final conclusion.

We've got a rigorous process of collecting data from new generators, benchmarking them, and retraining our models when necessary. Often retrains aren't needed though, since our accuracy seems to transfer well across a given deepfake technique. So even if new diffusion or autoregressive models come out, for example, the artifacts tend to be similar and are still caught by our models.

I will say that our models are most heavily benchmarked on convincing audio/video/image impersonations of humans. While we can return results for items outside that scope, we've tended to focus training and benchmarking on human impersonations since that's typically the most dangerous risk for businesses.

So that's a caveat to keep in mind if you decide to try out our Developer Free Plan.

Give it a try for yourself. It's free!

We have been working on this problem since 2020 and have created an trained an ensemble of AI detection models working together to tell you what is real and what is fake!

Thank you. As an inference-based detection platform, our models go into every scan with the assumption that all files are both not the original/ground truth AND the files have been likely transcoded. We never say something is 0% or 100% fake because we don’t have that ground truth. That said, our award-winning models are able to say, with a confidence score of 1-99% — the higher being likely manipulated — which, in turn, is sent to the team using said detection to action as they will. Some use it as one of many signals to make an informed decision manually. Others have chosen to moderate or label accordingly. There are experts who’ve been called to testify on matters like this one, and some of them work on these very models.

As for synthetic content that is undetectable to the naked eye or ear, we are already there.

You would need thousands to tens of thousands of images, not just 50 to produce an adversarial network that could use the API as a check.

If someone wanted to buy it, I'm sure reality defender has protection especially because you can predict adversarial guesses.

It would be trivial for them to build "this user is sending progressively more realistic, rapid responses" if they haven't built that already.

We see who signs up for Reality Defender and instantly notice traffic patterns and other abnormalities that allow us to see if an account is in violation of terms of service. Also, our free tier is capped at 50 free scans a month which will not allow for said attackers to discern any tangible learnings or tactics they can use to bypass our detection models.

Just based on the first post, where they talk about their API a bit, it sounds like a system hosted on their machines(?). So, I assume AI trainers won’t be able to run it locally, to train off it.

Although, I always get a bad smell from that sort of logic, because it feels vaguely similar to security through obscurity in the sense that it relies on the opposition now knowing what you are doing.

Data provinence would be neat and a big benefit. But any solution that requires virtually all content publishers to change approach (here: add signing steps to their publishing workflow) is doomed to fail. There is no alternative way to do this than what OP is doing, which is to try to filter the fire hose of content into real vs not.

> so, tie content to domains. A domain vouches for content works like that content having been a webpage or email from said domain. Signed hash in metadata is backwards compatible and its easy to make browsers etc display warnings on unsigned content, content from new domains, blacklisted domains, etc.

Okay, so I generate an image, open Instagram, take a picture of the generated image on a hi-res screen, and hit upload. Instagram dutifully signs it and shows it to the public with that signature. What does this buy us?

I understand the appeal of hashing-based provenance techniques, though they’ve faced some significant challenges in practice that render them ineffective at best. While many model developers have explored these approaches with good intentions, we’ve seen that they can be easily circumvented or manipulated, particularly by sophisticated bad actors who may not follow voluntary standards.

We recognize that no detection solution is 100% accurate. There will be occasional false positives and negatives. That said, our independently verified an internal testing shows we’ve achieved the lowest error rates currently available for addressing deepfake detection.

I’d respectfully suggest that dismissing AI detection entirely might be premature, especially without hands-on evaluation. If you’re interested, I’d be happy to arrange a test environment where you could evaluate our solution’s performance firsthand and see how it might fit your specific use case.

I'am also a bit shocked by this SDK approach, why not a simple API where you upload a file, get an ID and wait till it's done? Beside that, sometimes it works, sometimes not:

  {
      "request_id": "9622a21f-37bf-4404-ac84-8728977a5272",
      "status": "ANALYZING",
      "score": null,
      "models": [
          {
              "name": "rd-context-img",
              "status": "ANALYZING",
              "score": null
          },
          {
              "name": "rd-pine-img",
              "status": "ANALYZING",
              "score": null
          },
          {
              "name": "rd-oak-img",
              "status": "ANALYZING",
              "score": null
          },
          {
              "name": "rd-elm-img",
              "status": "ANALYZING",
              "score": null
          },
          {
              "name": "rd-img-ensemble",
              "status": "ANALYZING",
              "score": null
          },
          {
              "name": "rd-cedar-img",
              "status": "ANALYZING",
              "score": null
          }
      ]
  }

As noted elsewhere, we give confidence scores between 1-99%. We also use many different models for each modality for a more robust and complete answer with each scan, and each model has its own confidence score.

This is definitely a concern, but this is more or less how the cybersecurity space already works. Having dedicated researchers and a good business model helps a lot for keeping detectors like RD on the forefront of capabilities.

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Thank you! We’ve been working on this since 2021 (and some of us a bit before that), and we’re reminded every day that we are ultimately working something that helps people on the macro and micro level. We want a world free of the malevolent uses of deepfakes for ourselves, our loved ones, and everyone beyond, and feel all should be privy to such protection.

I understand this is in jest, but unfortunately AI generation tools more or less stopped the six-finger issue a couple of years ago. We are decidedly not a model used for the express detection of finger abnormalities, but a multi-model and multimodal detection platform — driven by our Public API (which you can try for free right now, btw) — which uses many different techniques to differentiate between content that is likely manipulated and likely not manipulated.

That said, neat gag.

I find that most companies find that to be a feature, not a bug. People are more likely to hit accept if they can only see a small chunk of the content.

And the scrolling behavior is infuriating

It's like scrolling through molasses.