Detection-Guided Adaptive Purification with Diffusion Models for Robust Audio Deepfake Detection
The Detection-Guided Adaptive Purification (DGAP) framework is a diffusion-based defense that dynamically adjusts audio purification strength based on how much an input's score shifts relative to a detector's response. It works by lightly purifying inputs and then applying stronger purification only to those that appear adversarial, achieving the best overall performance across various detectors while minimally affecting benign audio.