Video fingerprints vs. audio fingerprints: what to select for video recognition
Fighting piracy would be a breeze if every unauthorized copy came with official metadata and original audio. But pirated media is often distorted, renamed, or redubbed, making it hard to track. That’s where digital fingerprints come in. In this article, we’ll explore whether audio or video fingerprinting is better for identifying illegal video content.
Why compare fingerprints and not video files
When looking for copies of an original video, the first instinct is to compare the original file with potential copies. This involves analyzing the video content, such as frames, pixels, and audio, of two or more videos to determine if they are similar or identical.
While this method may work for comparing two files once, it can require a lot of time and resources when dealing with an entire library. Additionally, using video files for copy detection can compromise security since sharing video files with anti-piracy vendors is necessary.
Furthermore, comparing frame by frame can result in fewer matches, especially when dealing with distorted copies. For example, it’s possible to overlook illicit copies that were mirrored, zoomed, or cropped before the upload.
The good news is that there’s a lightweight, secure, and highly effective solution to overcome the problems of matching files. It’s digital fingerprints.
There are two main types of fingerprints: audio and video. Let’s see what’s the difference between the two types and what technology to select for copy detection.
Advantages of video fingerprints
Video fingerprinting is an innovative technology that uses digital fingerprints to quickly and accurately identify and compare content on a large scale. A video fingerprint is a unique line of code that represents a specific video file in a lightweight, secure, and efficient way.
There are several properties of video fingerprints that make them an excellent tool for fast and precise content matching:
- Lightweight
Video fingerprints are small in size by design and can be processed more quickly than the entire video file. They require much less storage than storing original files. - Use of visual features
Video fingerprints use perceptive features to determine the similarity between fingerprints of similar images. It solves the problem of matching not-exactly-equal videos. - Security
It is not possible to go back from a fingerprint to a video. Fingerprint generation is also a fully secure process that can be set up without sharing original video files. - Cost-effective
With fast and accurate matching, it’s possible to reshape a monitoring team to optimize costs and get more results at the same time. - Detection of distorted and renamed copies
Even if an illegal copy was renamed, redubbed, or somehow distorted, it will be identified by a fingerprint. These copies are impossible to detect manually so video fingerprints cover these common cases.
The major downside of video fingerprints is that they are difficult to generate. Few companies have the technology to create and match them at scale. At WebKyte, we have mastered this technology so our customers can automatically fingerprint and scan their entire video library.
Audio fingerprints as a half-measure
Digital audio fingerprints are unique representations of audio and video files that capture certain features, such as rhythm, melody, and tempo. These fingerprints are generated using complex algorithms that extract specific information from an original file and turn it into a condensed, digital signature that can be used to identify and compare audio and video content.
Digital audio fingerprints have a wide range of potential use cases, including content identification and copyright protection.
For example, music identification services such as Shazam use them to identify songs playing in the background of a video or on the radio. Similarly, content recognition services can use digital audio fingerprints to identify instances of copyright infringement by comparing audio tracks to a database of legal content.
While audio fingerprints are highly effective for audio content identification, when it comes to video matching there are some limitations to this technology.
As audio fingerprints only capture audio information, it means they may not be able to capture all of the visual information in a video file. This can limit their effectiveness in identifying certain types of video content. For example, if a copy is redubbed, which is a very popular case in international piracy, it will not be detected using audio fingerprints.
To detect video copies with changed audio, you need one video fingerprint or multiple audio fingerprints for every dub version. Using audio technology multiplies the number of fingerprints and makes matching longer.
Another field where audio fingerprints can drive false positives is content with minimal or reductive audio. For example, fashion shows, sports, and adult videos. In these cases, there may not even be enough distinct features to generate an audio fingerprint.
Additionally to these limitations, audio fingerprints are also not easy to create. Though there are more companies providing audio fingerprinting it’s still a half-measure for video identification as many copies are undetected by this technology.
Summary: what to select for video recognition
When it comes to video recognition, both types of fingerprints are commonly used. However, video fingerprints are often considered the better choice for several reasons.
Video fingerprints are more resilient to changes in audio, video quality, or resolution than audio fingerprints. This makes them more reliable for identifying copies of an original video, even if the copies have been somehow modified.
Using video fingerprinting technology will allow you to detect a higher number of copies, reducing the risks associated with using audio fingerprints. So, if you want to ensure the best results for your video matching needs, video fingerprints are the way to go.