Tech riderRev. 21 Sept 2026

DeepFake-O-Meter

Ch 176.4of 20AI Deepfake Detection Tools

1Runs onWeb
2Media typesimage, video, audio

2 lines stated Written from the maker's own pages: zinc.cse.buffalo.edu

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Overview

DeepFake-O-Meter is ranked #17 of 20 in AI deepfake detection tools on Specifiction. It runs on Web.

Compared on AI deepfake detection tools

Free plan: Yeszinc.cse.buffalo.edu
Media types: image, video, audiozinc.cse.buffalo.edu

Facts

Purpose: DeepFake-o-Meter aggregates results from research AI media detection models to support media authenticity assessment.zinc.cse.buffalo.edu · 7 Oct 2026
Detection models: The models page lists 37 integrated forensic research models.zinc.cse.buffalo.edu · 7 Oct 2026
Results: Each selected algorithm returns a percentage representing the likelihood that content was AI-generated.buffalo.edu · 7 Oct 2026
Model selection: Users can select detection algorithms using listed metrics that include accuracy, running time, and year developed.buffalo.edu · 7 Oct 2026
Open source: The platform is described as open source, with algorithm source code publicly accessible.buffalo.edu · 7 Oct 2026
User audience: The maker describes the platform as intended to make deepfake analysis available to the public, including social media users, journalists, and law enforcement.buffalo.edu · 7 Oct 2026
Submission sharing: Before upload, users are asked whether they want to share the media with researchers; the team says shared submissions can help train detection algorithms.buffalo.edu · 7 Oct 2026
Interpretation caveat: The platform provides analysis from multiple methods and does not make strong claims about whether uploaded content is authentic.buffalo.edu · 7 Oct 2026
Account access: The registration page offers account creation using an email, username, and password.zinc.cse.buffalo.edu · 7 Oct 2026
Support contact: The contact page lists [email protected] for contacting the lab.zinc.cse.buffalo.edu · 7 Oct 2026
Research models: The detection models page lists 37 integrated forensic research models.zinc.cse.buffalo.edu · 7 Oct 2026
Model categories: The model catalog includes methods labeled for image, video, and audio detection.zinc.cse.buffalo.edu · 7 Oct 2026
Multiple analyses: The University at Buffalo says users can run multiple detection algorithms, each returning a percentage likelihood that content was AI generated.buffalo.edu · 7 Oct 2026
Project team: The site identifies the UB Media Forensics Lab as the developer and lists Siwei Lyu as project director.zinc.cse.buffalo.edu · 7 Oct 2026
Intended users: The University at Buffalo describes the platform as serving users including social media users, journalists, and law enforcement.buffalo.edu · 7 Oct 2026
Result interpretation: The University at Buffalo says the platform provides analysis from a broad range of methods and leaves users to decide whether content is real.buffalo.edu · 7 Oct 2026
Model limitations: The model catalog notes that WAV2LIP-STA performance may suffer under heavy compression.zinc.cse.buffalo.edu · 7 Oct 2026
Current use: The home page reports that Deepfake-o-Meter was used on the AFP fact check website to detect a deepfake video.zinc.cse.buffalo.edu · 7 Oct 2026
Maker: The site attributes DeepFake-O-Meter to the University at Buffalo and the UB Media Forensics Lab.zinc.cse.buffalo.edu · 7 Oct 2026