But can't the bad actors use the same APIs to ensure that it's passing this first?
See my other response on this, but this is something we are actively protecting against.
Launch HN: Reality Defender (YC W22) – API for Deepfake and GenAI Detection
61–70 of 70 posts
Re: Launch HN: Reality Defender (YC W22) – API for Deepfake and GenAI Detection
#62Congrats on launching. Would be good to have a quick trial on the website for a sample image, rather than going through the SDK route.
This is something for us to consider in the near future!
Re: Launch HN: Reality Defender (YC W22) – API for Deepfake and GenAI Detection
#63Earlier quoted context omitted.
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!
I tried, It required making an account to use. In this day and age, everybody realises that forcing people to make an account does not count as free. It is paying with personal information.
I completely agree! Please see the bottom of our post where we offer free access in two ways:
1) Email up to 50 files to yc@realitydefender.com, we’ll scan them for you, no setup required
2) 1-click add to Zoom/Teams (via Appstore) to try detection live in your own calls immediately
Re: Launch HN: Reality Defender (YC W22) – API for Deepfake and GenAI Detection
#64Earlier quoted context omitted.
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!
I tried, It required making an account to use. In this day and age, everybody realises that forcing people to make an account does not count as free. It is paying with personal information.
Re: Launch HN: Reality Defender (YC W22) – API for Deepfake and GenAI Detection
#65Is 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.
That doesn't fix the fundamental potential for abuse, moral hazard, and accountability sink[1].
[1]: https://en.wikipedia.org/wiki/The_Unaccountability_Machine
Re: Launch HN: Reality Defender (YC W22) – API for Deepfake and GenAI Detection
#66Earlier quoted context omitted.
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…
I’m curious what awards the models have won?
We won multiple awards including RSA: https://www.rsaconference.com/library/press-release/reality-...
And we’ve published peer-reviewed research at top AI conferences E.g. CVPR, NeurIPS, ECCV, AAAI, Interspeech which are available at https://www.realitydefender.com/research
Re: Launch HN: Reality Defender (YC W22) – API for Deepfake and GenAI Detection
#67Are you sure you guys want to tackle this problem? This isn’t the type of thing where you build a product doing a lot of work up front, and then spend the rest of its life offering support and new features and relaxing a bit. You are committing to a cat and mouse game. You will constantly have to stay on top of ever improving tech that gets harder to beat, you will never know peace. You will have to exert more and mo…
This is something we're constantly updating, upgrading, iterating, and improving on. Every. Single. Day. Whether it's introducing new models, deprecating old ones, or improving existing ones, there is an element of both staying current but also looking ahead at research. Many of the new models generating hyperreal content we catch on day one because they're based on existing technology and/or research.
This sounds exhausting, and I don’t know what you will do when the day comes that you can’t keep up. Any day could be the end.
Re: Launch HN: Reality Defender (YC W22) – API for Deepfake and GenAI Detection
#68And fiction has become reality, this is a scenario explored in AI 2041
https://www.ai2041.com/
Re: Launch HN: Reality Defender (YC W22) – API for Deepfake and GenAI Detection
#69How 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.
Yes, of course new accounts should be scrutinized.
But what I find more interesting is how you prevent someone from training a model adversarially via one of your legitimate customers.
Wouldn't any of your customers that use your service to make a decision about something uploaded by a user be an attack vector?
Re: Launch HN: Reality Defender (YC W22) – API for Deepfake and GenAI Detection
#70I 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…
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.
And yet https took off when clear text became too much of a vulnerability