From Spectrograms to Shallow Speech SSL: Audio-Frontend Design for Generator-Disjoint and Low-Latency Audio--Visual Deepfake Detection

Abstract:Video-mediated conversational systems rely on synchronized speech and facial video, making audio--visual input integrity important for reliable perception. We study audio-frontend design in a fixed MRDF (Modality-Regularization-based DeepFake)-based pipeline with AV-HuBERT visual processing and an MSCT (multi-scale cross-modal transformer) fusion encoder. We compare spectrogram-style log-filterbank features with frozen wav2vec~2.0 and WavLM representations, dual-SSL fusion, executed depth, and shallow-layer aggregation. Across three training runs, full wav2vec~2.0 improves mean macro AUROC from $84.19\%$ to $87.19\%$ on generator-disjoint MMDF. Six-layer execution further improves detection, and learned non-uniform aggregation adds $2.53$ percentage points, reaching $90.61\%$. Shallow execution reduces measured inference latency relative to full-depth wav2vec~2.0 across the tested input durations; first-second evaluation reaches $86.85\%$ macro AUROC. In a single-run comparison, dual-SSL concatenation outperforms either single stream, while one-way cross-attention yields lower detection performance and higher latency than concatenation. Layer-weight analysis shows greater emphasis on early speech layers, while removing the selector's visual input has little effect on detection performance.

Sound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)

arXiv:2610.24138 [cs.SD]

(or arXiv:2610.24138v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2610.24138 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.