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ImgDetect – AI Image Detector to Identify Fake and AI-Generated Images

Hacker News

ImgDetect – AI Image Detector to Identify Fake and AI-Generated Images

Hi HN community, I recently built an app called *ImgDetect*, an AI-powered tool that detects AI-generated, manipulated, or fake images. How it works: - Upload an image (or paste a link). - ImgDetect analyzes the image using multiple AI models. - It provides a probability score and highlights possible manipulations. Why I built it: With the rise of AI-generated content, it's becoming harder to distinguish real from fake. I wanted to create a lightweight, privacy-focused tool that anyone can use without technical knowledge. Link: - App Store / Download: https://apps.apple.com/us/app/ai-image-detector-imgdetect/id... Features: - Fast AI detection - Privacy-focused: No image data is stored - Available on mobile and web I'd love to hear your feedback and suggestions! If you find any bugs or have ideas for improvement, please feel free to share. Thanks a lot for checking it out!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
83%83% 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: model, apple, apps · Missing: mac, agents, macos
37%37% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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.

Correct prediction on native model

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