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Launch HN: Reality Defender (YC W22) – Deepfake Detection Platform
104 points by bpcrd 10 months ago | hide | past | favorite | 88 comments
Hi HN, we’re Ben, Gaurav and Ali from Reality Defender (https://www.realitydefender.ai). We help companies, governments, and journalists determine if media is real or fake, focusing on audio, video and image manipulation. Our API and web app provide real-time scanning, risk scoring, and PDF report cards.

Recent advancements in machine learning make it possible to create images, videos and audio of real people saying and doing things they never said or did. The recent spread of this technology has enabled anyone to create highly realistic deepfakes. Although some deepfakes are detectable to the eye by experienced observers who look closely, many people either don’t have experience or are not always looking closely—and of course the technology is only continuing to improve. This marks a leap in the ability of bad actors to distort reality, jeopardizing financial transactions, personal and brand reputations, public opinion, and even national security.

We are a team with PhD and Master degrees from Harvard, NYU and UCLA in data science. Between us, we have decades of experience at Goldman Sachs, Google, CIA, FDIC, Dept of Defense and Harvard University Applied Research at the intersection of machine learning and cybersecurity. But our current work began with a rather unlikely project: we tried to duplicate Deepak Chopra. We were working with him to build a realistic deepfake that would allow users to have a real-time conversation with “Digital Deepak” from their iPhones. Creating the Deepak deepfake was surprisingly simple and the result was so alarmingly realistic that we immediately began looking for models that could help users tell a synthetic version from the real thing.

We did not find a reliable solution. Frustrated that we’d already spent a week on something we thought would take our coffee break, we doubled down and set out to build our own model that could detect manipulated media.

After investigating, we learned why a consistently accurate solution didn’t exist. Companies (including Facebook and Microsoft) were trying to build their own silver-bullet, single-model detection methods—or, as we call it, "one model to rule them all." In our view, this approach will not work because adversaries and the underlying technologies are constantly evolving. For this same reason there will never be a single model to solve anti-virus, malware, etc.

We believe that any serious solution to this problem requires a “multi-model'' approach that integrates the best deepfake detection algorithms into an aggregate "model of models." So we trained an ensemble of deep-learning detection models, each of which focuses on its own feature, and then combined the scores.

We challenged ourselves to build a scalable solution that integrates the best of our deepfake detection models with models from our collaborators (Microsoft, UC Berkeley, Harvard). We began with a web app proof of concept, and quickly received hundreds of requests for access from governments, companies, and researchers.

Our first users turned to our platform for some deepfake scenarios ranging from bad to outright scary: Russian disinformation directed at Ukraine and the West; audio mimicking a bank executive requesting a wire transfer; video of Malaysia’s government leadership behaving scandalously; pornography where participants make themselves appear younger; dating profiles with AI-generated pro pics. All of these, needless to say, are completely fake!

As with computer viruses, deepfakes will continue evolving to circumvent current security measures. New deepfake detection techniques must be as iterative as the generation methods. Our solution not only accepts that, but embraces it. We quickly onboard, test, and tune third party models for integration into our model stack, where they can then be accessed via our web app and API. Our mission has attracted dozens of researchers who contribute their work for testing and tuning, and we’ve come up with an interesting business model for working together: when their models meet our baseline scores, we provide a revenue share for as long as they continue to perform on our platform. (If you’re interested in participating, we’d love to hear from you!)

We have continued to scale our web app and launched an API that we are rolling out to pilot customers. Currently the most popular use cases are: KYC onboarding fraud detection and voice fraud detection (ie. banks, marketplaces); and user-generated deepfake content moderation (ie. social media, dating platforms, news and government organizations).

We are currently testing a monthly subscription to scan a minimum of 250 media assets per month. We offer a 30 day pilot that converts into a monthly subscription. If you’d like to give it a try, go to www.realitydefender.ai, click “Request Trial Access” and mention HN in the comments field.

We’re here to answer your questions and hear your ideas, and would love to discuss any interesting use cases. We’d also be thrilled to collaborate with anyone who wants to integrate our API or who is working, or would like to work, in this space. We look forward to your comments and conversation!

Are you concerned that your product will inadvertently improve deepfakes? Suddenly you've given them a baseline that they need to be better than, and hackers love challenges. I predict this will turn into a constant arms race like AV or copyright protection, and I don't think this will work in the long run.

IMO, KYC needs to go back to in person verification. Everything you can do digitally can be faked or impersonated.

In 2017 deepfakes were pretty crude, today the avg person can’t tell a real face from a deepfake generated on a 5 year old iPhone. We expect the tech to continue moving in this direction. So, similar to anti-virus, we're approaching this problem with an iterative, multi-model solution that can evolve with the threat.

So the folks at the forefront of deep fake technology (i.e. the attackers you're targeting) will slip through your product because it lags behind the state of the art (like AV, which you said is the approach you're following), while innocent folks will be caught by it due to a new kafkaesque version of "prove you're not a bot" since you focus on reducing false negatives. Hopefully I can avoid companies using your product.

Retrospective antivirus-esque techniques are still useful, though, as not every actor is a state-level actor, and even then, forcing state-level actors to "burn" their state-of-the-art exploits/models because previous exploits/models are detected out-of-the-box, slows down the abuse of those actors.

And realistically, since deepfake detection will inevitably be more expensive than captchas or antivirus scanning, this will be adopted by human-in-the-loop organizations for critical processes where threat scoring or moderation is already being applied.

That said - Reality Defender, please train your system on diverse human data sets, do not release models where ethnicity or gender (including gender identity) are nontrivially correlated with deepfake score, and have processes in place from day 1 to allow users to report suspected patterns of bias. The kafkaesque "prove you're not a bot" scenario envisioned by the parent poster is one thing for holistic human-in-the-loop verification processes, and another thing if it suppresses minority voices and minority access to government services.

We agree. Dataset fidelity and bias are major concerns for publicly available datasets. For this reason we are working to develop programmatically created datasets along with anti-bias testing and policies.

"Bias" and "anti-bias" is a slippery snake that will bite you as soon as it warms up to you.

Of course, because these companies are probably owned by the same people in the end that develop the DeepFake datasets, generating endless income from both sides.

It's like ADA Compliance lawsuits. I can't prove the AccessaBe or other "ADA Compliance" web tooling are generating these lawsuits, but their company would not exist without them. Why wouldn't they want more lawsuits?

The majority of large, popular datasets in deep learning are curated and hosted by academics:


Thanks, yes, we benchmark on these research datasets as well.

Biggest fear is people being put in prison based on deepfake video evidence.

At some point it might make video evidence inadmissible. I imagine a tool like Reality Defender offers could be used in court to tackle this, but now Reality Defender itself needs verification. Like all tools used to verify evidence I suppose. Is a negative from Reality Defender enough to prove that a video is real, or is it only valid proof when it's proving it's fake?

It's not about verification, it's about reasonable doubt.

Right, but verification is the opposite direction of doubt. It's not that verification is a boolean true or false for the evidence, it just helps remove doubt. For example, CCTV footage verified to be pulled from a service station by the police is less easy to doubt CCTV footage provided by a friend of the accused. Adding a verification paper trail to that makes it even harder to doubt. DNA test results provided by the defendant are worth nothing unless they're verified by a paper trail that's trusted.

So back to Reality Defender, why shouldn't we doubt Reality Defenders positive or negative results? There would need to be a period of verification and testing that "proves" within a reasonable margin that it works.

Great comments - Better results require more data, improving models, and onboarding new (different) models. All areas of focus for us!

I came here to write that. Me and my friends have a competition to get a (obviously CGI) picture of my living room filled with gold coins past those "is that photo real" platforms. So far, none has stood up to the test.

The worst one so far was TruePic, who even gave me a certificate of authenticity and then kept that certification PDF online on their website until they heard about us openly mocking them, and then they blocked the photo not because someone noticed that it's fake, but because "User has violated terms of service", which was me mocking them.

Maybe your objections triggered their Mock Object Detector?


We're up for the challenge! Send us your images / videos (ask@realitydefender.ai) and we'll send you the results!

I'll wait until you have a public API. The trick with reinforcement learning is that I need the feedback from what triggered your system to determine how to modify my image to bypass it.

If I was them I'd be concerned that it DOESNT move in this direction. Not a lot of money to be made selling one-and-done fake detection software, but a fortune available if they can convince clients a perpetual subscription is a core requirement.

We are optimizing compute requirements by isolating only the part of the media that requires scanning, so that we can pass along the cost savings to customers. At scale, the costs will low enough to scan all files just like anti-virus.

Aren’t you worried that in person KYC will lead to an improvement in latex face masks? Sounds like an arms race.

good point

I too wonder whether cryptographic signatures will be the long run solution to deepfakes. Can you outline why you don't think that will be the case?

Here's an alt solution to argue against:

1. The necessary PKI gets bootstrapped by social media companies, where deepfakes begin to seriously threaten their "all-in-on-video" strategy, and simultaneously look like an opportunity to layer on some extra blue-check-style verification.

2. Example: you upload your first video to twitter, it sees the AV streams are unsigned, generates a keypair for you, does the signing, and adds the pubkey half to your twitter account. (All of this could be done with no user input.)

3. The AV streams have signatures on short sequences of frames. Say every 24th video frame has a signature over the previous 24 frames embedded into the picture. Similarly, every second of audio has a signature baked in.

4. The signatures aren't metadata that can be easily thrown away. They're watermarked into the picture and audio themselves in a way that's ~invisible & ~inaudible, but also robust to downsampling & compression. This is already technically possible.

5. Since we're signing every past second of the AV streams, this works for live video.

6. Viewers on the platform see a green check on videos with valid signatures; maybe they even see the creator's twitter handle if this is a reshare.

7. Like all social media innovations, the other major platforms copy it within the next 6 - 12 months. POTUS uses it. People come to expect it.

8. Long run: the public comes to regard any unverified video footage with suspicion.

Why won't this deepfake solution come to pass in the long run?

(Not OP)

This would work for videos that people want to acknowledge as their own, but they could just tweet out they own it for the same affect.

The issue this tackles is videos that people don't want to claim ownership of, e.g. if a video emerged of <insert politician here> kicking a child, the politician can't say "I haven't signed it therefore it's not mine", instead we need tools like the above to be able to say, "this is faked, do not trust it".

Maybe in the interim we need deepfake detection tools, but I'm asking about the long run. Suppose signed AV takes off as described above. The public has been trained: "If the video doesn't have The King's Seal, it's not from The King."

I think the point is that yes, that seems like a good solution for verifying content that purports to be released by a certain creator, but it doesn't solve the problem of deep fakes for captured footage i.e. you can prove it isn't a video that you created, but you can't prove it isn't a video someone else took of you.

That makes sense. But if signed AV takes off, the video someone else took of you and shared likely bears their seal. And audiences decide how much they trust that source – just like they look for a CNN / BBC / etc logo in the corner currently.

Great discussion. Many have asked the same questions.

We hope a standard will be created and used by all digital content creation tools. But this will take time. And even then, bad actors will not be deterred. They will always find ways to create fake content and pass it as authentic. We want to be there to fight them every step of the way!

There is a standard for this actually. Hot(ish) off the press: https://c2pa.org/

Not OP, but personally I'd love to see this sort of tech baked directly into smartphones and cameras. Your camera could have tamper-proof hardware that embeds signatures like you describe into the metadata of your video/photos while you take them. Sure, the user can separate it, but like you said over time people just won't trust photos/videos without this assurance.

Apps like snapchat that let you apply filters could also add additional metadata, going through more hardware verified operations provided by things like Apple's secure enclaves, saying something like "X filter applied over frames N-M" and you can have an edit chain to go with the original video/photo.

With this infrastructure in place our phones could really serve as ways to certify reality itself.

No, absolutely not. This will only serve to prop up a false confidence in correctly signed videos. So in the end you're giving all the power to black hat groups and three letter services that can procure the secret keys from the camera vendors. Face it: Video proof is as good as dead, to delude yourself into thinking anything else is extremely dangerous.

I believe deep-fakes to be a serious threat to functioning democracies around the world. I would love to be on the front-lines fighting against this threat.

I have submitted my resume: https://cphoover.github.io/

Have you also considered either offering a browser plugin to display contextual warnings attached to video elements? Or thought of working with browser makers? The web/social media is where a ton of fake media is propagated. It would be good if large platforms (fb/twitter/yt) integrated with such service, but also individuals should be able to protect themselves.

I worry about the same thing.

I think "blockchain" (or even just a signed, immutable, public database) is mostly a solution in search of a problem, but I do think it may have an application here.

If you can hash a video when it's recorded and publish the hash with a timestamp that can't be forged, you can at least prove that this video existed at least as long ago as that stamp.

That allows you to invalidate any deepfakes produced on top of that video that have later timestamps.

It's not perfect, but it might be one weapon against this stuff.

You can have an immutable centralized/federated system without using "crypto" though "proof-of-work", there are databases out there that provide immutable storage that don't require this overhead.

Providing api access to a signed immutable database makes sense... but I'm not sure how much sense utilizing existing popular cryptocurrencies would make (e.g. bitcoin, etherium)

You need cryptography for hashing and verifying the integrity of any copy of the database.

You probably don't need proof of work, you're right.

Proof of work is not needed, just a pool of time servers that you can make a quorum out of. We're working on exactly this on top of roughtime (https://roughtime.googlesource.com/roughtime) for a future version of QR Date (https://qrdate.org/)

Great discussions! Some thoughts:

First, there are multiple solutions to this problem, all need to be explored and many will have a part in the future. Bad actors will do everything they can to find a way around every solution.

Second, for the hashing approach, have a look at "Perceptual Hashing", it's a way to hash content like a an image, even if the resolution changes.

Where to store the hashes? A centralized server is probably fine, but there is always a risk of a bad actor exposing it somehow. A secure blockchain can work better. But if you go that route, might as well go with the most secure blockchain. POW is generally more secure than POS. And currently the most secure blockchain is Bitcoin. So one solution is to batch hashes together and write them with a Bitcoin transaction in some cadence.


I've been thinking about this -- original video could be the NFT, and the purpose of the system would be to verify / validate the content (instead of the weird pyramid schemes it's being used for today).

The issue is with the hash, esp with lossy compression algos. If the video is re-transmitted somewhere, its content remains effectively "the same" (eg, it is that particular person, saying these exact things, at some specific time and place). A regular hash will not match as the video is different bit-to-bit though.

So for this scheme to work, we would need some kind of a "domain hash", something that is very precise in aspects that matter, and fuzzy in ones that don't. In case of videos, one dimension could be the text transcript. But even then it falls apart pretty quickly.

In that scenario, where is the line between a deepfake and satire?

That's an irrelevant issue. The issue I'm talking about is only whether a viewer can determine if a video has been edited.

Once they know it's edited, they can decide for themselves whether it's enjoyable satire or an attempt to deceive them.

We are adding new roles to our careers page on www.realitydefender.ai but feel free to reach out to career@realitydefender.ai and we can discuss your interest!

We are working with few partners (including Microsoft) who are interested in integrating our solution. We are focused right on supporting large organizations (companies and governments) that need to scan user generated content at scale.

Congratulations on the launch team! This is a great need and hope the team can deliver.

My questions are:

1 - Do you have any research on false negatives and false positives for your platform?

2 - How do you build trust in your platform so that users will use your results (and their users will trust it)? Fake news has been so widespread and people continue to believe in it, so why is that any different than with deep fake?

3 - Why are you trying a consumption-type of pricing? Cybersecurity typically charges on a per seat, and it would be very hard for a malware provider charge by 'malware detection'

1- Each model looks for different deepfake signatures. By design, the models do not always agree, which is the goal. We are much more concerned with false negatives, and we target a min of 95% accuracy for our model of detection models. 2 - The challenge is educating users about results without requiring a PhD. Our platform is targeted for use by junior analysts in cyber security or trust and safety. 3 - This is a good suggestion. We are exploring how we can offer an unlimited plan that can cover our high compute costs (we run our multiple models in realtime).

Why would you be more concerned about false negatives? Wouldn't false positives erode trust and value in your product, and considering the applications you're targeting, possibly open you up to lawsuits if you start accusing innocent people of being deepfakes (which, IMO, currently seems unlikely)?

We provide a probabilistic percentage result that is used by a trust and safety team to set limits (ie. flag or block content) so it is not a binary yes/no. We search for specific deepfake signatures and we explain what our results are identifying.

So... that percentage sort of 'return type' allows the people using your service to decide how aggressive they want to be? Smart. It also could possibly turn your service into more of a tool and less of something that someone could blame incorrect results on.

First of all, congratulations on the lauch! Your description about the "model of models" and combining their scores is really intriguing. Detecting deepfake is an interesting topic on its own and apparently there are lots of use cases that I'm not even aware of, partly due to my limited knowledge in this subject. There are a few points of I'm curious about (Beware that the following questions can be very silly, coming from someone having little to no experience in the field):

- What do you use as input for the model? Does it use all the pixels in all the frames in the input video? How about the video's metadata (location, extension,...)?

- My biggest concern about fighting deepfakes is that they have a point to achieve where the line between reality and fiction is nonexistent. Namely, if a deepfake video of someone can be created to look exactly like a real one if that someone decide to record such a video, I imagine there would be no way to tell the deepfake video from the authentic one (since there is no difference between the two). Because of that, this looks like a losing battle to me, but maybe I'm just too pessimistic. Do you feel that it is a real problem? Do you believe it is such a long shot that we shouldn't be worried about, or even if things reach that point, there would still be tools in our arsenal to counter such technologies.

Thanks for your questions.

- The input to our models are image, video, and audio. Based on the model, we can use parts of the image (esp faces) or whole image. Yes, we also incorporate metadata for better detection.

- It's a fair concern. As quality of generative media increases, so does the sophistication of detection. Since, we fully understand how generative media is created, it gives our the leverage to reverse engg. Much like the anti-virus industry (wrt scanning), we'd need to be at the forefront of not only detection, but generation methods, re-learn models based on new generation methods, etc.

Thanks for the response, I'll look more into this. So what you're saying is we need to understand the methods to devise countermeasures to such methods, instead of being model-agnostic. That sounds like a grueling task and truly an arms race. Best of luck to the team!

> After investigating, we learned why a consistently accurate solution didn’t exist.

There is no way to prove 100% solution can exist. If a similar tool is used to verify the evidence of a video in legal setting, then it's just a matter of time an innocent person ends up behind the bars. The only way is for the society to adapt to the fake videos not to rely on a tool that can never be correct.

It's my impression that this tool can only disprove a video. The fact that the tool returns a "Not fake" is not the same as proving it's veracity, a point I would hope a judge would include a jury instruction on, when it becomes part of a trial.

Imho this is a good product, but wouldn’t it make more sense to simply sign videos cryptographically?

Unfakeable and unbeatable.

So someone uploads a video, you sign it, they display video. If authenticity is in question check the signature.

Deep fake detection is intractable imo. Use cryptography instead.

Hell if you want to be thorough sign each frame and create an extension for YouTube and other providers to literally check to see if a given frame or period was altered.

Most videos people watch nowadays don't come from "official" authorities, whose public keys are known and signatures can be verified. When videos are recorded by random people from their smartphones, what signature are you going to verify?

Even if smartphone manufacturers start integrating digital signatures right into their cameras, you can use the smartphone to re-record a pre-recorded fake video. And I'm sure that with enough resources you can do something way more clever.

I really don't see how crypto can solve this problem. I don't think AI can either (for reasons already mentioned in this thread). It's something we'll have to learn to live with.

> Most videos people watch nowadays don't come from "official" authorities

I doubt this is true. Most videos are likely disseminated from a handful of companies.

> Even if smartphone manufacturers start integrating digital signatures right into their cameras, you can use the smartphone to re-record a pre-recorded fake video. And I'm sure that with enough resources you can do something way more clever.

Even if a fake video was re recorded it would have a different signature than the “original”. Problem solved.

Literally cryptography is the only solution

" you can use the smartphone to re-record a pre-recorded fake video."

I assume the intent is not to store solely a direct hash of the original video, but also the original file which can be fingerprinted, or the fingerprint itself that will be matched if a later duplicate is uploaded.

Fingerprint differing from hashing in that two non-identical, but similar files (e.g. an original video and deepfake) can have the same fingerprint, but differing checksums.

We’re fascinated by the potential applications of crypto in content provenance. In this example, a UGC video platform would need a way to initially determine the content hasn’t been manipulated before it’s signed, right? What about a live scenario where a deepfake mimicking an exec calls a manager to wire $10M (https://www.forbes.com/sites/thomasbrewster/2021/10/14/huge-...)

We totally recognize deepfake detection is a big & constantly evolving challenge, but we don't see that as a reason to cede the truth to bad actors :)

> "We’re fascinated by the potential applications of crypto in content provenance. In"

@bpcrd what's the advantage of using a block-chain to store video fingerprints, to determine provenance, over say a highly performance-optimized immutable centralized or federated system?

There are advantages to both, as well as using existing blockchains (Ie. Bitcoin) to record small amounts of information via Merkle Trees.

Fun fact: The oldest blockchain pre-dates bitcoin....and started in the 1990's in the New York Times classified via a daily recorded hash value!


Hey - great and important idea.

Have you discussed / looked into sampling environmental radio noise at various frequencies and locations and then interpolating samples of them within the video and audio itself at recording-time?

(ideally along with some kind of near-unfalsifiable timestamp signals and/or device keys to confirm that "yes, this unique device was here at this time and the proof is within the pudding)

Currently, we are consider image, video and audio data types. This multi-modal idea sounds interesting none the less, thanks for sharing :)

There are companies that already provide this kind of service so it might be interesting for you to combine efforts:


Thanks! You're welcome. The noise signature could be included in-band, and wouldn't necessarily require multiple machine learning models to verify.

You're right, in-band noise signatures is a pretty cool idea. We take inspiration from digital signal processing techniques when building our ML products.

Grand. Take with a grain of salt, but it's possible you could find https://osf.io/m43tg/ worth a look.

Hi, congratulations to your launch.

I would also like to ask three question. Do you know how well your model generalizes to video/audio deepfakes created by models that are not within your training sets? And also have you investigated whether your model can be used in a GAN setting to improve a deepfake generator towards creating better fakes? Or how robust your detectors are against adversarial attacks?

Great questions.

1 - We include multiple models for GAN and non-GAN related synthetic media.

2 - Models are only as good as the training data, and most training data breaks down in the real world because hackers have access to this same open source training data. So we create our own proprietary training data which we have automated, and we continuously update it based upon emerging deepfakes that we find in the wild.

3 - We target 95% accuracy with all public and proprietary training sets. And we continuously test and iterate both the data sets and the models.

4 - Our policies require a background check on all users to filter out bad actors. We additionally have technology safeguards in place to limit improper use.

Great idea, and a very necessary thing. Congratulations on the launch!

There's a guy on Reddit who restores old photographs in an interesting way. He says he finds bits and pieces in recent stock images, that he cuts and pastes and adjusts until it looks perfect. So it's a kind of deepfake HI (vs. AI).

Here's an example of his work: https://i.redd.it/eeoz0hvvwvo81.png

Maybe not your target market ATM -- although this talent could be used to make very convincing fake images.

Thank you for the pointer, the example image restoration looks incredible. Happy to know more about different ways media fakes are being created nowadays.

We have solved this problem already for text with digital signatures. There might be a way to digitally sign speech in real-time and show the signature on a display device, which is captured on video. It could be just a smartphone app or a separate device. This way every video could be proven authentic, even if they originate from unofficial sources.

We hope a standard will be created and used by all digital content creation tools. But this will take time. And even then, bad actors will not be deterred. They will find ways to create fake content and pass it as authentic. We want to be there to fight them every step of the way!

How do you hash something so fuzzy as video / speech? Two different microphones picking up the same audio will result in two digital recordings that hash differently.

That's the problem. It would require some kind of new fuzzy signature algorithm, which works on information content in speech, rather than raw audio data. In this case, exact matching of data isn't important, but major changes in information, so that changing / removing / adding words wouldn't be possible. Simple way would be to just convert speech to text first with a known method, so that both microphones could work from the same source data. The display device could actually show real-time transcription of the speech along with the signatures.

This is super cool, you should chat with the folks in Estonia who had worked on developing Sentinel (https://thesentinel.ai/) which had a similar premise but ended up pivoting. I advised them for a bit, happy to chat too (email in bio).

Edit to include link to their website

Awesome! We will look them up! And happy to chat with you. You can also email us at ask@realitydefender.ai

Thank you!

So how much cash/clout are you guys raking in from nation states wishing to disguise their homebrew deepfake tech?

This is definitely an interesting problem, and I can see a place for it.

I want to comment (in ignorance because I don't know the techniques you are using) that there is more to detecting fakes or "misinformation" than just the digital attributes of the data. Specifically, confirmation from multiple sources, reputation of sources, and above all, consistency with a world model of how people behave. For example, if there's a video of Biden backing Putin, you could dismiss it as fake regardless of video attributes.

I think (and have been criticized for saying, why I don't understand), that education and emphasizing critical thinking are the biggest counters to fakes, not learning to spot them in feature space. I believe that whatever you make, sanity checks need to be a part of it, and not just blind flagging of true / false based on the digital attributes.


Great points! This is a problem that must be tackled from multiple directions. Different tools that flag content and general education.

We plan to guide our users in this way, incorporating these different tools and providing general education.

I would like to coin this alternative name: Deeptect

Have you looked at identifying AI written content?

If you plan to release this, I will be your first customer.

Yes - Please contact us at ask@realitydefender.ai

Would be great to be able to invest into YC companies like these via a HN syndicate

May I ask who has worked in CIA for decades and then became a startup guy ?

Congratulations on the launch. What is a good email to reach you at?

Thank you! Please reach out to ask@realitydefender.ai :)

This is very promising! Congratulations!

Thank you!

This looks amazing!

Thank you! We hope we can help make the world a safer place!

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