  With recent advances in speech synthesis including text-to-speech (TTS) and
voice conversion (VC) systems enabling the generation of ultra-realistic audio
deepfakes, there is growing concern about their potential misuse. However, most
deepfake (DF) detection methods rely solely on the fuzzy knowledge learned by a
single model, resulting in performance bottlenecks and transparency issues.
Inspired by retrieval-augmented generation (RAG), we propose a
retrieval-augmented detection (RAD) framework that augments test samples with
similar retrieved samples for enhanced detection. We also extend the
multi-fusion attentive classifier to integrate it with our proposed RAD
framework. Extensive experiments show the superior performance of the proposed
RAD framework over baseline methods, achieving state-of-the-art results on the
ASVspoof 2021 DF set and competitive results on the 2019 and 2021 LA sets.
Further sample analysis indicates that the retriever consistently retrieves
samples mostly from the same speaker with acoustic characteristics highly
consistent with the query audio, thereby improving detection performance.
