Detecting Audio Deepfakes on the Edge:Lightweight SSL-Based Detection in a Browser Plugin

Audio deepfakes are a growing challenge for the general public, as well as for journalists and fact-checkers.

The latter need reliable tools to verify the authenticity of their sources, while at the same time keeping their information private.

Commercial deepfake detection solutions rely on cloud-based processing, which raises privacy concerns.

To solve this problem, we propose an on-device audio deepfake detection model.

We show that a truncated self-supervised backbone with a simple logistic classifier is both very fast and often more accurate than existing solutions.

Our solution outperforms the baseline AASIST by 10% and improves inference speed by 40%.

We integrate this model into a browser plug-in, which allows journalists and fact-checkers to detect deepfakes easily and securely.

Code for the plugin is available at this https URL.