Do you know what watermarking is in voice biometrics? It’s a method of digitally tagging audio. It involves embedding an inaudible marker, called an identifier, into an audio file. The goal is to protect the recording from unauthorized use and verify its authenticity.
Watermarking is a tool that significantly improves the security of voice biometrics systems, mainly by preventing voice-based attacks, so-called deepfakes.
In one of our tools, we developed this proprietary method, a unique technique that protects audio recordings from being used for voice synthesis or access. The method is currently in functional use.
Tag: biometrics
Phase 2 of the Vesper project, a biometrics-based voice communicator, is nearing completion. During this phase, we worked on creating audio stream augmentation technology. We wrote about what this augmentation is here https://biometriq.pl/en/voice-stream-augmentation-what-is-it/
Our proprietary voice stream augmentation engine is currently undergoing perceptual (listening) and blind testing. Their goal is to provide an objective evaluation to confirm proper engine operation in line with the established quality parameters. Furthermore, the built-in voice stream augmentation technology in the voice messenger is designed to aid in detecting unauthorized voice use for further synthesis/conversion without causing degradation of sound to the human ear. This is all to prevent voice theft and ensure the most effective service performance.
It’s worth noting that solutions on the market such as SKYPE, ZOOM, DISCORD, Google Meet, TEAMS, WhatsApp, Signal, Threema, Viber, and Telegra do not support biometric caller authentication.
We are pioneers in this regard.
The comprehensive project completion is scheduled for September 2026.
In the first stage of the project, we mainly tested the far end voice stream source authenticity algorithm, which we informed you about here https://biometriq.pl/en/tests-of-a-voice-communicator-with-a-source-authenticity-detection-module-are-underway/
You can read more about the project on the website https://biometriq.pl/en/vesper-save-voice-communication-platform-with-integration-of-biometric-services/
Project financed by EU funds.
Are you curious about the final solution?
The first international standard for age-assurance technology has been published – ISO/IEC 27566-1:2025. This document establishes a framework for age-assurance systems and describes their core features, including privacy and security, to enable age-based eligibility decisions.
Access permissions refers to the term that authorizes access to applications or services. Definitions of age verification, age estimation, age inference, and subsequent validation are available here.
The standard’s main initiator is Tony Allen, head of the UK Age Check Certification System (ACCS), founder of the Global Age Assurance Standards Summit, and leader of the Australian Age Assurance Technology Research (AATT). He calls the publication of ISO 27566-1:2025 (which he co-authored) “a significant breakthrough in age assurance at the global level.”
A sample of the ISO 27566-1:2025 standard is available free of charge, but access to the full version of the document requires purchase. https://www.iso.org/standard/88143.html
more about the standard https://www.biometricupdate.com/202512/first-international-standard-on-age-assurance-sees-publication
source, photo https://www.biometricupdate.com
- The voice biometrics market is relatively young, currently estimated at USD 2-3 billion, USD 2.6 billion according to the Mordor Intelligence report “Voice Biometrics Market Size, Forecast Report, Landscape 2025”.
- Depending on the source, forecasts assume growth of approximately $10-15 billion over the next 8-10 years.
- The leading region is North America – in the Fortune Business Insights analysis, the share in 2024 was nearly 37%.
- Asia-Pacific (APAC) is often cited as the fastest growing region in the coming years.
- The “Healthcare and Life Sciences” sector will be the leader in 2025 with a 40% market share.
- Growth is driven by: growing security requirements, the need for passwordless authentication, the development of voice and AI technologies, and the digitization of financial and contact services.
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What distinguishes effective voice biometrics systems? The following four indicators determine the advantage of one system over another:
1. Accuracy rate, it means that the effectiveness of biometric systems should be in the range of 95-99%.
2. FAR (False Acceptance Rate), a metric that measures how often a system incorrectly accepts an unauthorized person (e.g., someone impersonating a user) as a valid user. In the most accurate systems, this rate is less than 1%. The lower the rate, the more secure the system and the more difficult it is to impersonate.
3. FRR (False Rejection Rate), a metric that measures false rejections, or the number of times the system rejects a genuine user when it should accept them. Ideally, this figure is below 3%.
4. EER (Equal Error Rate). The point at which the FAR equals the FRR, this metric is often used to compare the quality of biometric systems.
The most effective systems are generally considered to be Phonexia oraz ID R&D systems due to their outstanding performance in comparative tests.
In our research, we primarily use Phonexia engines, but we also utilize others such as Kaldi (X-vector) and ECAPA. The goal is to test our algorithms as extensively as possible in a diverse environment. Security is our top priority.
It turns out that deepfake audio can be more dangerous than video! According to the Pindrop report, in the two years 2023-2024 there was a 760% increase in the number of such deepfakes (audio).
In an era of increasing attacks, self-awareness seems to be a key barrier protecting humans from these types of threats. It is about:
● limited trust in voice assistants,
● knowledge of social techniques used by fraudsters,
● control over the content you publish on the Internet.
In system solutions, it is obvious to use advanced biometric technologies and methodologies to detect deepfakes in real time.
For example, Pindrop uses a technique called acoustic fingerprinting as one of its capabilities. This involves creating a digital signature for each voice based on its acoustic properties, such as pitch, tone, and cadence. These signatures are then used to compare and match voices across calls and interactions. For more on deepfakes, check out this podcast with Vijay Balasubramaniyan, CEO of Pindrop. Link below
https://www.biometricupdate.com/202504/biometric-update-podcast-digs-into-deepfakes-with-pindrop-ceo
As a reminder, Pindrop is a company based in Atlanta, USA. Their solutions are leading the way for the future of voice communications, setting the standard for identity, security, and trust in every voice interaction. More at pindrop.com
Phase 1 of the Vesper project is nearing completion. We’ve launched a test version of the messenger with an implemented far-end voice stream authentication module. Tests are being conducted on three different environments: Windows, Android, and iOS. The results are consistent with the project’s KPIs. We’re working to ensure that quality indicators not only meet the design minimums but, where possible, exceed the established goals. Our priority is to develop a product that meets user needs and builds a positive user experience.
We conduct experiments based on 40 speakers, 20-second recordings, testing each recording across 5 channels, and obtaining over 171,500 embeds. This number of recording configurations is designed to help achieve the target parameters, confirming the effectiveness of our messenger.
Vesper Messenger is intended to be a response to the growing problems of cybersecurity and identity theft.
More about the project https://biometriq.pl/en/vesper-save-voice-communication-platform-with-integration-of-biometric-services/
How to effectively detect voice-based fraud? How to distinguish a real voice from a fake one, e.g. one generated on the basis of AI? The answer is simple. This requires advanced voice biometrics tools and a number of analyses. We publish here two examples that we analyzed some time ago in our laboratory and, thanks to our proprietary algorithm, we assessed with a very high probability whether the voice is real or fake and to what extent it is consistent with the voice of a given person.
The analyzes concern:
● recognizing the voice of one of President Duda’s Russian pranksters pretending to be President Macron
● assessment of the similarity of the voices of actors Piotr Fronczewski and Filip Pławiak in the film Rojst. In the play, men play the role of the same person (Kociołek) in adulthood and youth, respectively.
We share with you the conclusions from these experiments.
Biometric comparison of the voices of Fronczewski and Pławiak.
For this purpose, we used 25 seconds of total speeches by both characters, composed of several fragments of their original speech, based on the original film soundtrack. What compliance did we achieve?
The results of the analysis showed that the actors’ biometric voices are NOT consistent. Pławiak statement vs. Fronczewski VP – only 15% agreement, Fronczewski statement vs. Pławiak VP – 11%, but interestingly, these differences are not noticed at the level of the ear. In our opinion, the voices of Pławiak and Fronczewski are almost identical. And that is ultimately what this is all about.
For both characters, gender and nationality were recognized with minimal uncertainty (score of almost 100%). An age difference between the characters was also detected, estimated at 20 years.
Analysis of the voices of Russian pranksters Vladimir Kuznetsov (Vovan) and Alexei Stolyarov (Lexus) impersonating President Macron.
In this case, we biometrically analyzed the recordings of the pranksters’ voices and compared them with the voice of the real Macron (in both Polish and English versions). We downloaded all voice samples in the form of individual recordings from the public domain on YouTube. Our goal was to confirm the effectiveness of biometric systems for this specific situation – identifying fraud.
It turned out that the voice of one of the “Lexus” pranksters was just over 50% consistent with the voice of the President of France and as much as 97% consistent with the voice of the false president. The voice of the second one – “Vovana” – showed no similarities (0%) to the fake president.
This clearly proves that thanks to biometric analysis we managed to:
●detect the fact, only after 1 minute, that a fake president was involved in the conversation
● identify the identity of the fictional president (Lexus)
● confirm that the public domain is a very good source of voice samples, which may not always be used for noble purposes
● strengthen the thesis that the most effective attacks are those using social engineering, and in this case it was the choice of the right time when the President was faced with increased stress (rocket fall).
These are just selected examples of the use of specialized biometric tools to confirm the identity of people. If implemented in the future, they may help detect voice-based abuse.
As many as 230 million stolen traditional passwords were registered despite meeting the standard requirements regarding their complexity (min. 8 characters, 1 capital letter, 1 digit, and a special character), according to Specops Breached Password Report z 2025 r. This means that the level of traditional security is insufficient and more effective protection tools are needed. Can a biometric password prove to be a more secure password? Absolutely yes!
Biometrics is one of the safer ways of logging in because it is based on biometric features of people such as face, eye pupil or voice. Biometric identifiers are unique to a given person and distinguish them from others.
The advantage of biometrics lies in its unrivaled accuracy and convenience. Unlike traditional methods such as passwords or PINs, which can be easily forgotten or stolen, biometric identifiers are inextricably linked to people. This inherent link between individuals and their biometric characteristics makes it much more difficult for unauthorized individuals to impersonate another person.
An example of secure login using biometrics is VoiceToken from BiometrIQ, a voice authentication tool that provides very strong, two-step authentication. We remind you how it is done.
When speaking words, the compliance of the read words (first level) with the pattern is verified as well as the biometric compliance of the speaker’s voice with his VoicePrint (second level).
Extremely high security is ensured by an algorithm for selecting words to be read, which reduces the possibility of guessing the sequence of words that will be displayed on the screen to almost zero. The Speech To Text (STT) mechanism combined with an innovative biometric engine guarantee high effectiveness, even in the case of attacks based on speech synthesis.
Are you ready for changes?
We start the New Year with something interesting for fans of football and other live events. The typical, traditional ticket may soon be replaced by biometric identity verification. Research shows that nearly 50% of sports facilities have such a plan.
MLB’s ticketless “Go-Ahead Entry” system with biometric verification can reduce waiting times to enter the facility by almost 70%.
That’s why biometric authentication at sporting events could become a hit. The challenge, as always with large projects, is the financial aspect related to the costs of system implementation and investments in specialized equipment, training and tool integration. It is this aspect, according to the PYMNTS report, that may determine the implementation of biometric solutions by smaller facilities.
Go-Ahead Entry is MLB’s patented ticketless entry system using biometrics provided by NEC. The statistics come from a 2023 pilot at Citizens Bank Ballpark in Philadelphia, which showed biometric lanes move 68% faster and allow 2.5 times more people to pass than the fastest lane using physical or smartphone tickets.
We are curious how this situation will develop. Do you think this is a good idea?
More you find out here article