Adaptable Deepfake Detection

Adaptable deepfake detection is critically needed due to the rapid and continuous evolution of Text-to-Speech (TTS) models, particularly in voice cloning. These advancements have made it possible to synthesize highly realistic speech that can closely mimic a specific speaker’s voice, often with just a single reference utterance. This capability, while beneficial for various applications, also introduces significant security and ethical concerns.

Here’s a breakdown of why adaptable deepfake detection is essential:

• Evolving Threat Landscape:
  • Highly Realistic Deepfakes: Modern voice cloning models can generate speech that is nearly indistinguishable from an original speaker’s voice from only a short audio sample.
  • Malicious Exploitation: This technology has been exploited in various malicious activities, including fraud, misinformation, and identity theft. Examples include fraudsters mimicking corporate executives for financial transactions and scammers posing as family members to coerce money.
  • Accelerating Rate of New Models: New TTS models are introduced at an accelerating rate. Attackers can also modify existing TTS models with a relatively small amount of data using techniques like parameter-efficient fine-tuning (e.g., LoRA).
• Limitations of Current Detectors:
  • Poor Generalization to Unseen Models: While current Audio Deepfake Detection (ADD) algorithms easily detect deepfakes from known TTS models, they may generalize poorly to deepfakes from previously unseen generation models. This poor performance on unseen TTS models like 11Labs is a significant security concern.
  • Degradation on Unseen TTS: Performance on seen TTS is often almost perfect, but it degrades considerably on unseen TTSs, highlighting a critical gap in current detection capabilities.
• Practical Challenges of Data Collection:
  • Limited Access to Commercial Models: Many leading commercial TTS models, such as 11Labs, are only accessible via paid APIs, which limits the feasibility of large-scale data collection for retraining detection models.

Therefore, a detection system must be capable of efficiently adapting to novel deepfake generation methods with minimal data, allowing it to continuously evolve against new threats in a dynamic and evolving environment. This is precisely what few-shot adaptive frameworks like ADD-GP aim to achieve, offering effective few-shot adaptation for new TTSs and enhanced personalized detection.