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What is AudioContext fingerprinting?
Admin
2025-03-17T09:49:10.417Z
One of the components made up of browser fingerprinting is AudioContext fingerprinting. Let's figure out how it works and its importance in fingerprinting.
1. The overview of AudioContext fingerprinting
1.1. What is AudioContext fingerprinting?
- AudioContext: Based on the definition from CSS Tricks, all sounds should be managed and played within an AudioContext. Establish one or more audio signals and link them to the audio output offered by the AudioContext instance in order to generate sound using the Web Audio API.
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AudioContext fingerprinting: It is commonly referred to as an "audio fingerprint," a hash-derived representation of the audio context on your computer. According to your browser's audio settings and available hardware, a website requests it to imitate a sinusoidal curve for displaying sound files. As supplementary entropy for browser fingerprinting, this sinusoid is transformed into a hashing algorithm and transmitted to servers.
AudioContext fingerprinting's definition
1.2. How does AudioContext fingerprinting work?
With the support of an audio stack in the OS and the audio card, AudioContext offers a variety of audio signal handling capabilities from signal generation to signal to the process. In particular, current fingerprinting research produces a triangular wave using an oscillator node, then transmits the wave to a signal processing node called a dynamics compressor node to produce a compression impact. The audio signal is then transformed into the frequency domain using AnalyserNode.
2. AudioContext fingerprinting modes
In AudioContext fingerprinting, there are two key aspects: Noise mode, and Off mode.
2.1. Noise mode
With the help of Genlogin antidetect browser, you can control your AudioContext read-outs by adding a persistent random noise to the readout or allowing the website to see the actual audio fingerprint of your device. Genlogin will alter the browser's audio stack by turning on the Noise mode in the AudioContext section, probably resulting in a distinctive audio fingerprint.
Noise mode in Genlogin interface
Notes:
- The website will notice that the audio fingerprint hash is not persistent over numerous launches if you create a browser profile with AudioContext masked set to Noise and access it on various computers with different hardware setups.
- The additional noise lingers. In addition to the existing computer fingerprint, it is introduced as a filter. Hence, if the equipment has been modified, the readouts will also have been altered.
2.1. Off mode
Websites will be able to hear your computer's actual audio fingerprint if AudioContext masking is turned off. When 100% unique AudioContext monitoring systems cause websites to respond poorly, switching the option to Off can be beneficial.
Off mode in Genlogin interface
3. The impact of AudioContext fingerprinting
Producing sound from a mobile browser and device audio stack is quietly complex. In AudioContext fingerprinting, a website uses the technique called AudioContext API to send a low-frequency sound through the browser to the device and measures how the device processes that data. This helps inform how to process audio – but no audio is recorded, collected, or played, so you don’t need microphone and speaker access. And yet, this technique can inform fingerprinting with multiple parameters and values. Additionally, with the help of AudioContext fingerprinting, you can opt for audio sources, apply effects to audio, build audio visualizations, use spatial effects (like rotation), and much more through various platforms.
4. Conclusion
As there are other copies of your system and audio stacks elsewhere, audio fingerprint hashes are not original. If you reveal your actual audio fingerprint, you belong to the same group of people with the same audio hardware setup. Also, changing other fingerprints makes it easier for websites to recognize your browser profiles as distinct individuals. Run Genlogin on your devices to further minimize the unpredictability of your browser profiles, improving their capacity to fit in with the general distribution of users.
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