# Mitigating Backdoor Triggered and Targeted Data Poisoning Attacks in Voice Authentication Systems

arXiv: https://arxiv.org/abs/2505.03455 · HTML: https://mlsanity.com/paper/2505.03455/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2025.

## Built on by (most cited first, 1 of 1)

- [A Survey of Threats Against Voice Authentication and Anti-Spoofing Systems](https://mlsanity.com/paper/2508.16843/) (2025)

## Abstract (arXiv)

Voice authentication systems remain susceptible to two major threats: backdoor triggered attacks and targeted data poisoning attacks. This dual vulnerability is critical because conventional solutions typically address each threat type separately, leaving systems exposed to adversaries who can exploit both attacks simultaneously. We propose a unified defense framework that effectively addresses both BTA and TDPA. Our framework integrates a frequency focused detection mechanism that flags covert pitch boosting and sound masking backdoor attacks in near real time, followed by a convolutional neural network that addresses TDPA. This dual layered defense approach utilizes multidimensional acoustic features to isolate anomalous signals without requiring costly model retraining. In particular, our PBSM detection mechanism can seamlessly integrate into existing voice authentication pipelines and scale effectively for large scale deployments. Experimental results on benchmark datasets and their compression with the state of the art algorithm demonstrate that our PBSM detection mechanism outperforms the state of the art. Our framework reduces attack success rates to as low as five to fifteen percent while maintaining a recall rate of up to ninety five percent in recognizing TDPA.