Audio Deepfake Detection
Overview
Audio deepfake detection asks whether a recording is genuine, synthetically generated, or partly manipulated, and can also locate altered regions or identify their source. Systems range from handcrafted spectral features and CNN or graph-based detectors to self-supervised speech encoders such as WavLM and XLS-R, with contrastive learning, domain adaptation, and mixtures of experts used to improve transfer. Benchmarks such as ASVspoof and In-the-Wild test speech detectors, while newer tasks cover partial speech edits, music, singing, and environmental sounds; audio-visual systems can also use synchronization and cross-modal consistency. A central challenge is that strong results on familiar, clean datasets often fail to transfer to unseen generators, languages, codecs, and real-world mixtures, making robustness, calibration, fairness, and realistic evaluation ongoing concerns.
Latest papers
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