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

realitydefender.com

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

#31

Post by bpcrd »

DonHopkins wrote: post #28

How easy is it to fool Reality Defender into making false positives? Whenever I'm openly performing nefarious illegal acts in public, I always wear my Sixfinger, so if anyone takes a photo of me, I can plausibly deny it by pointing out (while not wearing it) that the photo shows six fingers, and obviously must have been AI generated. In support of said nefarious illegal acts, the Sixfinger includes a cap-loaded grena…

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.

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

#32

Post by Eisenstein »

bpcrd wrote: post #21
taneq wrote: post #4

Yeah but does it actually work, though? There have been a lot of online tools claiming to be "AI detectors" and they all seem pretty unreliable. Can you talk us through what you look for, the most common failure modes and (at suitably high level) how you dealt with those?

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 gene…

What's the lead time between new generators and a new detection model? What about novel generators that are never made public?

I think the most likely outcome of a criminal organization doing this is that they train a public architecture model from scratch on the material that they want to reproduce, and then use without telling anyone. Would your detector prevent this attack?

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

#35

Post by bobbiechen »

bpcrd wrote: post #23
seanw265 wrote: post #20

How do you prevent bad actors from using your tools as a feedback loop to tune models that can evade detection?

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.

How would you detect someone who tests a single image using a new free tier, and then (if successful) uses that image against a targeted customer account?

Working in a similar area (bot detection) I think it's very difficult to proactively stop such targeted attacks, but maybe in this space you can do something interesting like duplicate detection across a consortium.

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

#36

Post by bpcrd »

RainyDayTmrw wrote: post #34

Is this gonna be Turnitin[1] all over again? [1]: https://www.nytimes.com/2025/05/17/style/ai-chatgpt-turnitin...

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.

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

#37

Post by bpcrd »

bobbiechen wrote: post #35
bpcrd wrote: post #23

Earlier quoted context omitted.

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.

How would you detect someone who tests a single image using a new free tier, and then (if successful) uses that image against a targeted customer account? Working in a similar area (bot detection) I think it's very difficult to proactively stop such targeted attacks, but maybe in this space you can do something interesting like duplicate detection across a consortium.

We'd rather not tip our hand on any/all techniques used to discern actual users from bad actors and those seeking to reverse engineer, but suffice to say we do have are methods (and plenty of them).

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

#39

Post by mitthrowaway2 »

bpcrd wrote: post #30
primitivesuave wrote: post #17

First want to say that I sincerely appreciate you working on this problem. The proliferation of deepfakes is something that virtually every technology industry is dealing with right now. Suppose that deepfake technology progressed to the point where it is still detectable by your technology, but is impossible for the naked eye. In that scenario (which many would call an eventuality), wouldn't you also be compelled to…

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 t…

Have you checked the calibration of that confidence value? When it reports 99% confidence, are 99/100 of those manipulated?

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

#40

Post by yjftsjthsd-h »

Grimblewald wrote: post #14

I feel like a much easier solution is enforcing data provinence. Ssl for media hash, attach to metadata. The problem with AI isnt the fact its ai, its that people can invest little effort to sway things with undue leverage. A single person can look like 100's with signficantly less effort than previously. The problem with ai content is it makes abuse of public spaces much easier. Forcing people to take credit for wor…

> 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?

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