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Fake or real? Try our AI image detector

Hacker News

Fake or real? Try our AI image detector

Hey HN! We're Ayman and Dylan, co-founders of Nuanced ( https://www.nuanced.dev/ ). We want to share a tool we’re working on to detect fake and real images: https://trial.nuanced.dev/demo/ . The UI is bare-bones but you’ll get the idea. Drag or upload an image and our tool will display the probabilities with which it thinks that the image might be AI-generated or not. If you want, you can click “No, it’s AI” to confirm that the image was AI-generated, or “No, it’s real” to confirm that the image was not AI-generated. Why we’re working on this: as AI-generated images continue to blur the line between real and artificial and their adoption and quality rises, so too does the risk for fraud and misinformation. Not being able to trust what you see online threatens whatever level of "realness" or authenticity online material has. Companies like dating apps, news sites, and trust and safety teams have a growing need to distinguish AI-generated images from authentic ones. The models we built are trained on various architectures, such as Dalle-3, Midjourney, and SDXL, with continuous integration of data from the latest AI image generators. Our technology can detect deepfakes and verify user profile images, documents, IDs, or media images. Additionally, it can detect fake or counterfeit products, services, or experiences being marketed on e-commerce platforms. We hope it’s fun and would be very interested in any cases it gets wrong, as well as whatever else you’d like to ask or say!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, user · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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