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Video fingerprinting explained
WebKyte Video Fingerprint is a compact representation of a video that contains a digital description of selected frames. It’s a small file created from a video, where each fingerprint is unique to the original video.
Generation of video fingerprints is conducted during a multi-step process that includes the detection and extraction of key video frames and encoding of their visual features to the binary representation. It’s a completely secure process that can be done without sharing original video content.
Video fingerprints are irreversible to the original files and cannot be identified without metadata.
When matched with other fingerprints, it enables rapid identification of copies of the video or copies of its separate parts.
Video fingerprinting in action
Video fingerprinting technology has been proven to be highly effective for automating mass-scale video identification, filtering user-generated content, and ensuring compliance with copyright regulations.
Precise video matching
By using video fingerprints instead of video files, the process of content matching becomes significantly faster, delivering real-time results with near-zero false positives.
High accuracy
The usage of video fingerprints makes the probability of false positive results
is nearly zero.
is nearly zero.
Built for scale
Video fingerprinting technology excels at handling large volumes of video content. It’s easier and faster to store and process compact representations of videos when it comes to massive amounts of data.
Secure by design
Video fingerprints are irreversible to the original files providing the highest level of content security. It’s impossible to identify the content without metadata.
Cost-effective
Automation of content recognition eliminates the need for extensive teams of analysts. A single content security professional can manage large amounts of content simultaneously.
Distortion resistant
Video fingerprints rely on video frames identifying content regardless of metadata, audio, and different distortions.
Supported video distortions
Identify copies regardless of their audio, metadata,
and video distortions
and video distortions
Quality changes / CAMRip
Mirroring
Dynamic zooming
Horizontal and vertical sidebars
Slight rotation or cropping
Logos and drawings overlays
Interface elements, ticker, etc.
Color modifications
Time inconsistencies such as playback
Slight speed increase/decrease
Video fragments cut
Deepfake
Video fingerprinting
vs. other video recognition methods
Video fingerprinting detects the biggest number of video copies in comparison to other methods
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