Research Study
We are building a research corpus of authentic camera footage to study the signal patterns that distinguish genuine video from AI-generated or manipulated media.
This project focuses on technical signal analysis of authentic video.
This study investigates the forensic signal patterns present in authentic camera-captured video. The goal is to improve authenticity analysis of video and better understand how real footage behaves after compression, editing, sharing, and platform processing.
By analysing these patterns across many clips, devices, and scenarios, we can build more reliable forensic tools for assessing whether a piece of video is genuine, manipulated, or synthetically generated.
Modern synthetic media — including AI-generated video — makes this increasingly important. Understanding what authentic footage actually looks like at a technical level is the foundation of reliable detection.
Many video detection systems are trained on clean or limited datasets. Real-world footage is far messier. It is resized, recompressed, cropped, screen-recorded, and shared through social platforms and messaging apps.
To understand genuine video properly, we need real camera-captured clips from the wild — recorded on real phones, in real lighting, with real compression, from real contributors. That diversity is what makes a forensic corpus reliable.
Short original clips captured by a phone, action camera, DSLR, mirrorless camera, or other real recording device are all useful.
✓B-roll and outtakes
✓Test footage and lens checks
✓Behind-the-scenes clips
✓Everyday phone recordings
✓Action camera footage
✓DSLR / mirrorless clips
✓Drone footage
✓Indoor and outdoor scenes
Clips do not need to be cinematic or polished. Short clips are useful. Even 2–3 clips from a single device can help. The only requirement is that the footage was captured by a real camera or phone — not AI-generated.
Uploaded clips are used strictly for forensic research analysis. We analyse technical characteristics of the video signal, including:
Contributors may choose to provide a website, portfolio, YouTube channel, or other URL. Contributors who do so can be acknowledged in the final study.
Where appropriate, backlink credit can be included for contributors who wish to be identified. Attribution is entirely optional — you can contribute anonymously if you prefer.
Please avoid uploading sensitive, highly private, or confidential footage. Clips should be safe to store for research purposes.
Read the study details
Understand the purpose, what clips help, and how footage is used.
Upload original camera clips
Submit short real-world footage from any phone, camera, or recording device.
We analyse the signal patterns
Clips are processed for compression artifacts, encoding patterns, and motion consistency.
Contributors may be credited
If you choose to be identified, you can receive credit and a backlink in the final study.
Upload short original camera clips to help build a real-world video authenticity dataset. Every clip improves the quality of forensic analysis research.
Upload Your ClipsContributions are voluntary. You can remain anonymous or receive credit in the final study.