Definition of audio deepfakes

Audio deepfakes are synthetic speech samples generated using AI models to mimic human voices. They are increasingly used for fraud, impersonation, and misinformation, making robust detection essential.

Key challenges:

High realism: Modern models (e.g., ElevenLabs, PlayHT) produce near-human speech.
Cross-lingual threats: Fake audio spans multiple languages.
Speaker variability: Need detection independent of specific speaker profiles.

🗣️ Types of Deepfake Generators & Attacks

Deepfake audio can be created using two main approaches:

Voice Cloning Generators: Replicate a specific person’s voice using a short audio sample as reference. Commonly used by commercial and open-source tools to mimic real individuals.
Text-to-Speech (TTS) Generators: Convert written text into speech with synthetic voices that are generic or prebuilt.

Open-Source Generators

These are models that are open-sourced to the community. Examples include:

F5-TTS: Multilingual zero-shot voice cloning (English, Chinese, Russian, Arabic)
xTTS-v2: Coqui's multilingual zero-shot voice cloning

Commercial API Services

These are some commercial models that may be misused by bad actors, for cloning voices:

ElevenLabs: High-quality commercial voice cloning
PlayHT: Enterprise-grade TTS services

🧠 Attack Sophistication Levels

High-Quality Deepfakes: Nearly indistinguishable from authentic speech - require lots of data and SOTA generators.
Medium-Quality Spoofs: Detectable with advanced algorithms
Low-Quality Fakes: Poor quality fakes that don't resemble target speaker but are easy to make by non-technical people.

🌐 Online Examples

Below are real-world demonstrations of audio deepfakes. While the exact tools or models used to create these samples are not disclosed, they illustrate the realism and potential risks of voice‑cloning technology.

Anderson Cooper, 4K Original/(Deep)Fake Example

Deepfake Example. Original/Deepfake Elon Musk.

🧑‍💻 Our Technology

Our deepfake audio detector is built on behavioral AI, a speaker‑agnostic system that doesn’t rely on voice‑prints or spectro‑based artifacts alone. Instead, it analyzes the behavioral dynamics of speech; such as intonation, emotional tone, hesitation patterns, that are often missing or distorted in synthetic audio.

This is achieved by training models on a mix of authentic (bonafide) speech and synthetic (spoofed) data, enabling the system to learn general patterns that distinguish real from fake audio. The detection process involves multiple stages: the audio is segmented into utterances, key speech features are extracted, and these features are evaluated by the model to assess whether the audio is bonafide or spoofed.

You can learn more on how to try our demo on your own.