ant-research

ant-research / Awesome-AIGC-Image-Video-Detection

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A curated collection of the latest research and resources on AI-Generated Image and Video Detection.

github.comant-researchAwesome-AIGC-Image-Video-Detection ai-generated-detection ai-generated-image-detection aigc-detection awesome-list multimodal-large-language-models
43
2
100% credibility
Found Apr 07, 2026 at 43 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A curated collection of datasets, benchmarks, research papers, competitions, and practical tools for detecting AI-generated images and videos.

How It Works

1
🔍 Discover the List

You search online for ways to spot AI-made images and videos, and stumble upon this helpful collection of resources.

2
📖 Browse Hot Topics

You scroll through recent news stories about fake AI videos and images to understand why detection matters.

3
📊 Explore Datasets and Benchmarks

You check out lists of real and fake image/video collections that researchers use to train detectors.

4
📚 Dive into Research Papers

You find hundreds of studies explaining new ways to tell real media from AI-generated ones, grouped by smart methods.

5
🛠️ Find Ready-to-Use Tools

You spot simple websites where you can upload your images or videos to check if they're AI-made.

6
✅ Try a Detection Tool

You visit one of the listed sites, upload a suspicious photo or clip, and get a quick verdict.

🎉 Spot Fakes Confidently

Now you know how to verify media authenticity and stay ahead of AI tricks in news and social posts.

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Star Growth

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AI-Generated Review

What is Awesome-AIGC-Image-Video-Detection?

This awesome curated collection gathers the latest research, resources, datasets, benchmarks, papers, and practical tools for detecting AI-generated images and videos in the AIGC space. It solves the chaos of tracking fast-evolving deepfake tech by organizing everything into hot events, benchmarks with direct downloads, categorized papers, competitions, and ready-to-use detection services. Developers get a one-stop hub—no code to run, just links to datasets like HydraFake or tools like Hive Moderation.

Why is it gaining traction?

Unlike scattered arXiv searches or generic deepfake repos, this stands out with structured tables for 40+ benchmarks (image, video, multi-modal) and papers split by MLLM-based reasoning vs. classification methods, plus real-world hooks like Netanyahu deepfake news. The Ant Group team's competition wins (e.g., NTIRE 2026 1st place) and open datasets add credibility, making it a quick ramp-up for prototyping detectors without reinventing wheels.

Who should use this?

AI forensics researchers benchmarking new models against GenVidBench or WildFake. Security engineers at platforms like social media or e-commerce building AIGC filters. Content moderators verifying viral videos before they spread misinformation.

Verdict

Bookmark it for the comprehensive curated collection—docs are thorough in a single README, but low maturity shows in 43 stars and 1.0% credibility score. Strong team backing makes it worth watching as AIGC detection heats up.

(178 words)

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