01 / Audio
Audio deepfake detection
Check audio recordings for signs of voice cloning and synthetic speech. Understand whether a recording contains AI-generated or altered audio.
Cloned voices · Generated speech · Altered audio
Deepfake detection
Fast, accurate deepfake detection across audio, video, and images, with in-depth authenticity analysis.
Sample analysissample.jpg
Synthetic score
Likely synthetic
0.94/ 1
0 · Real capture1 · Synthetic
Put your eyes to the test
One of these images is real. Can you tell which one?
ROUND 01 50 images to explore
Round 1. Choose the image you think is real. 0 selected.
Image sources and credits
- Image 1 · Zhenglin Huang et al. / So-Fake-Set · CC BY 4.0
- Image 2 · Zhenglin Huang et al. / So-Fake-Set · CC BY 4.0
- Image 3 · Zhenglin Huang et al. / So-Fake-Set · CC BY 4.0
Source images resized for display; analysis visualizations added. Both retain the source license.
Test on my
own data
Bring your images, audio, or video. See deepfake detection on your data.
Book a demoMultimodal detection
Audio, video, and image deepfake detection.
Check synthetic voices, manipulated footage, and generated images. Understand the authenticity of the media in front of you.
02 / Video
Video deepfake detection
Analyze videos for signs of AI generation or manipulation across visual and audio content. Check face swaps, altered footage, and generated clips.
Generated video · Face swaps · Manipulated footage
03 / Images
Image deepfake detection
Detect signs of AI generation and manipulation in photos and images. Examine synthetic portraits and edited images beyond what is visible at a glance.
Generated images · Synthetic portraits · AI edits
How it works
From media to a clear detection result.
Submit a file. Analyze it for signs of synthesis. Get a result that makes the next decision easier.
01
recording.wavvideo.mp4image.jpgChoose the media.
Start with the audio recording, video, or image you want to check.
02
Analyze for synthesis.
Detection models examine the media for patterns associated with AI generation or manipulation.
03
Sample result
Likely synthetic
0.940 · Real capture1 · SyntheticRead the result.
Review the detection label and synthetic score to understand how likely the media is to be generated or altered.
The synthetic score runs from 0 to 1. A score near 0 points to a real capture; a score near 1 points to synthetic media.
Detection benchmarks
High-accuracy deepfake detection.
Published evaluations of the models behind audio, video, and image detection.
01
Audio accuracy
99.47%
Externally scored
4,524 audio clips · English
02
Video accuracy
98.2%
Internal evaluation
1,000 video files
03
Image accuracy
95.77%
Internal evaluation
1,000 image files
Published model evaluations, not an end-to-end Rupt deployment benchmark. Image and video results are internal evaluations awaiting external validation. Performance varies with media quality, dataset, and operating threshold.
Request the benchmark
Get the evaluation details behind these results, including datasets and methodology.
Applications
Deepfake detection use cases
Use deepfake detection on its own or connect its results to other Rupt products.
KYC and liveness
Check submitted media for manipulation during onboarding. Pair detection with identity verification and a live-person check.
Explore KYCGround truth comparison
Add a separate reference check when you need to match a live selfie against a trusted photo of the person.
Explore reference matchingInvestigations
Bring media authenticity findings into an investigation alongside claims, source material, and other evidence.
Explore investigationsIllustrative workflows
A closer look
Good questions. Clear answers.
Deepfake detection analyzes media for signs that it was generated or manipulated by AI. It checks the media itself, including synthetic speech, generated images, and altered video, then returns an authenticity assessment.
Yes. Detection covers audio, video, and images. Each media type is analyzed for relevant signs of synthesis or manipulation, such as patterns in generated speech or visual artifacts in images and video.
The synthetic score runs from 0 to 1. Values near 0 indicate a real capture; values near 1 indicate synthetic media. Use it alongside the detection label to decide which files need further review.
The figures on this page are published evaluations of the underlying detection models. Audio is publicly benchmarked; video and image figures come from internal model evaluations pending external validation. Benchmark accuracy is not a guarantee for every file: media quality, compression, generators, and your operating threshold can affect results.
Yes. Compression, background noise, cropping, and repeated editing can change the evidence available to a detector. Use the highest-quality source available and review uncertain results rather than treating every score as conclusive.
No. Deepfake detection analyzes the submitted media for signs of AI generation or manipulation. Comparing a person with a trusted reference is a separate identity-verification task and can be added to the broader workflow when needed.
No. Deepfake detection answers whether media appears synthetic or manipulated. KYC checks a person's identity, and investigations examine claims and other evidence. These products can use detection results within their own workflows.
Book a demo to test deepfake detection against your media and discuss formats, volume, and integration requirements with the Rupt team.
Deepfake detection
Test it on your media.
See how Rupt detects AI-generated and manipulated audio, video, and images. Bring your use case. We'll walk you through the results.