# Symmetric Saliency-based Adversarial Attack To Speaker Identification

arXiv: https://arxiv.org/abs/2210.16777 · HTML: https://mlsanity.com/paper/2210.16777/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 30 Oct 2022.

## Builds on (most cited first, 18 of 18)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [In defence of metric learning for speaker recognition](https://mlsanity.com/paper/2003.11982/) (2020)
- [Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems](https://mlsanity.com/paper/1911.01840/) (2019)
- [Speaker Recognition Based on Deep Learning: An Overview](https://mlsanity.com/paper/2012.00931/) (2020)
- [Enabling Fast and Universal Audio Adversarial Attack Using Generative Model](https://mlsanity.com/paper/2004.12261/) (2020)
- [Adversarial Attacks on GMM i-vector based Speaker Verification Systems](https://mlsanity.com/paper/1911.03078/) (2019)
- [Adversarial Attack and Defense Strategies for Deep Speaker Recognition Systems](https://mlsanity.com/paper/2008.07685/) (2020)
- [The Attacker's Perspective on Automatic Speaker Verification: An Overview](https://mlsanity.com/paper/2004.08849/) (2020)
- [Universal Adversarial Perturbations Generative Network for Speaker Recognition](https://mlsanity.com/paper/2004.03428/) (2020)
- [Study of Pre-processing Defenses against Adversarial Attacks on State-of-the-art Speaker Recognition Systems](https://mlsanity.com/paper/2101.08909/) (2021)
- [Discriminator-Free Generative Adversarial Attack](https://mlsanity.com/paper/2107.09225/) (2021)
- [FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances](https://mlsanity.com/paper/2011.08483/) (2020)
- [Learning to fool the speaker recognition](https://mlsanity.com/paper/2004.03434/) (2020)
- [Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion](https://mlsanity.com/paper/2206.09131/) (2022)

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

- [Interpretable Spectrum Transformation Attacks to Speaker Recognition](https://mlsanity.com/paper/2302.10686/) (2023)

## Abstract (arXiv)

Adversarial attack approaches to speaker identification either need high computational cost or are not very effective, to our knowledge. To address this issue, in this paper, we propose a novel generation-network-based approach, called symmetric saliency-based encoder-decoder (SSED), to generate adversarial voice examples to speaker identification. It contains two novel components. First, it uses a novel saliency map decoder to learn the importance of speech samples to the decision of a targeted speaker identification system, so as to make the attacker focus on generating artificial noise to the important samples. It also proposes an angular loss function to push the speaker embedding far away from the source speaker. Our experimental results demonstrate that the proposed SSED yields the state-of-the-art performance, i.e. over 97% targeted attack success rate and a signal-to-noise level of over 39 dB on both the open-set and close-set speaker identification tasks, with a low computational cost.