# SENTRY: A Streaming Edge Network Trust Engine for Real-Time Audio Deepfake Detection

arXiv: https://arxiv.org/abs/2610.24468 · HTML: https://mlsanity.com/paper/2610.24468/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Anti-spoofing and adversarial audio attacks, one of 11 directions in Speech and audio language models.
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 17 Oct 2026.

## Placed in

- Speech and audio language models / [Anti-spoofing and adversarial audio attacks](https://mlsanity.com/speech/anti-spoofing-and-adversarial-audio-attacks/)

## Builds on (most cited first, 13 of 13)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection](https://mlsanity.com/paper/2109.00537/) (2021)
- [End-to-end anti-spoofing with RawNet2](https://mlsanity.com/paper/2011.01108/) (2020)
- [Does Audio Deepfake Detection Generalize?](https://mlsanity.com/paper/2203.16263/) (2022)
- [Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation](https://mlsanity.com/paper/2202.12233/) (2022)
- [WaveFake: A Data Set to Facilitate Audio Deepfake Detection](https://mlsanity.com/paper/2111.02813/) (2021)
- [RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing](https://mlsanity.com/paper/2111.04433/) (2021)
- [Audio Deepfake Detection: A Survey](https://mlsanity.com/paper/2308.14970/) (2023)
- [Human Perception of Audio Deepfakes](https://mlsanity.com/paper/2107.09667/) (2021)
- [People are poorly equipped to detect AI-powered voice clones](https://mlsanity.com/paper/2410.03791/) (2024)
- [Evaluating AI Models' Capability to Automate Voice Phishing Attacks](https://mlsanity.com/paper/2607.09970/) (2026)

## Abstract (arXiv)

Real-time voice cloning has enabled large-scale streaming deepfake and vishing attacks, while existing Network Detection and Response (NDR) solutions rely on GPU-based post-processing that requires buffered audio and generalizes poorly to unseen speech synthesis models. This paper presents SENTRY, a hardware-oriented, streaming trust engine for inline deepfake detection at the network edge. SENTRY employs an integer lifting-wavelet front end together with a bank of multiplier-free transforms, including wavelet-packet envelopes, Walsh-Hadamard sequency profiles, waveform micro-statistics, and long-term spectral features, designed so that acoustic analysis needs only integer add, shift, compare and bitwise operations. A supervised gradient-boosted tree classifier is combined with an unsupervised Mahalanobis out-of-distribution detector trained solely on authentic speech, and their outputs are fused by a lightweight logistic layer to produce graduated LOG, CHALLENGE, or DROP actions. Under a closed-set protocol, SENTRY reaches 98.75% accuracy at a 1.25% equal-error rate (EER) on WaveFake, with perfect separation of HiFi-GAN and WaveGlow; its supervised track alone achieves an AUC of 0.9994 (fused: 0.9993). On ASVspoof 2019 LA (attacks A01-A06) over an 8 kHz mu-law telephony channel, it reaches 94.75% accuracy at a 5.05% EER, with a supervised-track AUC of 0.9948 (fused: 0.9884) and per-attack EERs below 7%. In our prototype, the feature front end runs as bit-exact integer code on one Arm Cortex-A53 core, and the classifier and fusion stage are synthesized for a Xilinx Zynq UltraScale+ MPSoC using no DSP slices, returning a decision 220 ns after a feature vector is ready. These results indicate that deepfake screening can be placed inline without GPUs or floating-point hardware; generalization to unseen generators is left to future work.