Can Current Detectors Catch Face-to-Voice Deepfake Attacks?

TL;DR

This paper evaluates the vulnerability of current audio deepfake detectors to FOICE-generated voices, revealing their failure and proposing fine-tuning strategies to improve detection while highlighting generalization challenges.

Contribution

It provides the first systematic evaluation of FOICE detection, introduces fine-tuning methods to enhance detection accuracy, and analyzes the trade-offs in generalization to unseen voice synthesis methods.

Findings

Leading detectors fail to identify FOICE deepfakes under various conditions.

Fine-tuning improves detection accuracy significantly.

Trade-offs exist between specialization to FOICE and robustness to other generators.

Abstract

The rapid advancement of generative models has enabled the creation of increasingly stealthy synthetic voices, commonly referred to as audio deepfakes. A recent technique, FOICE [USENIX'24], demonstrates a particularly alarming capability: generating a victim's voice from a single facial image, without requiring any voice sample. By exploiting correlations between facial and vocal features, FOICE produces synthetic voices realistic enough to bypass industry-standard authentication systems, including WeChat Voiceprint and Microsoft Azure. This raises serious security concerns, as facial images are far easier for adversaries to obtain than voice samples, dramatically lowering the barrier to large-scale attacks. In this work, we investigate two core research questions: (RQ1) can state-of-the-art audio deepfake detectors reliably detect FOICE-generated speech under clean and noisy conditions, and (RQ2) whether fine-tuning these detectors on FOICE data improves detection without overfitting, thereby preserving robustness to unseen voice generators such as SpeechT5. Our study makes three contributions. First, we present the first systematic evaluation of FOICE detection, showing that leading detectors consistently fail under both standard and noisy conditions. Second, we introduce targeted fine-tuning strategies that capture FOICE-specific artifacts, yielding significant accuracy improvements. Third, we assess generalization after fine-tuning, revealing trade-offs between specialization to FOICE and robustness to unseen synthesis pipelines. These findings expose fundamental weaknesses in today's defenses and motivate new architectures and training protocols for next-generation audio deepfake detection.