---
# System prepended metadata

title: Adversarial Attack on Speaker Recognition Models

---

# Adversarial Attack on Speaker Recognition Models

## Steps to follow:
### Speaker recognization 
#### Three tasks:
1. close-set identification (CSI) - Recognizes unknown speaker from set G(enrolled speaker set).
2. open-set identification (OSI) - it may consider a speaker as an imposter for unenrolled people . 
3. speaker verification (SV) - one speaker can be enrolled ()

Target attack : More specific about the output.
Untargeted attack: Not much specific about the output , but targetted within the set(my understanding).

AS2T attack produces an adv. voice as input.

1. How to construct adversarial voices given arbitrary source and/or target speakers, considering that the adversary may own different levels of knowledge about SRSs?
Designing adversial voices as a 
AS2T is designed to adaptively choose the optimal loss function for each attack setting.
* Computing loss on trasformaed voice
2. Launching practical over the air attacks (distortions from air and hardware resp.)- enhance the robustness of adversarial voices against these distortions under various attack conditions, thus improve the practicability of AS2T.
3. Black box attacking : ======================================================


#### SPEAKER RECOGNITION SYSTEMS
https://github.com/clovaai/voxceleb_trainer/tree/master/loss



#### CNN
##### Model
GE2E + Sincnet

#### GE2E
(GE2E) loss, which makes the training of speaker verification models more efficient than our previous tuple-based end-to-end (TE2E) loss function. Unlike TE2E, the GE2E loss function updates the network in a way that emphasizes examples that are difficult to verify at each step of the training process. Additionally, the GE2E loss does not require an initial stage of example selection. With these properties, our model with the new loss function decreases speaker verification EER by more than 10%, while reducing the training time by 60% at the same time. We also introduce the MultiReader technique, which allows us to do domain adaptation - training a more accurate model that supports multiple keywords (i.e., “OK Google” and “Hey Google”) as well as multiple dialects.


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#### Resources:
* [ESAPA for SR](https://pythonawesome.com/unofficial-reimplementation-of-ecapa-tdnn-for-speaker-recognition/)
* [Speech brain](https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb)
* [Recognition with pre-trained model](https://colab.research.google.com/drive/1VF8H0fbuLe_QnBzqr3IRHS-0VSipw-g5?usp=sharing)
* [Some adversarial models](https://github.com/cleverhans-lab/cleverhans/tree/master/cleverhans_v3.1.0/examples/adversarial_asr)

* [audio_adversarial_examples](https://nicholas.carlini.com/code/audio_adversarial_examples)
* [Adversarial attacks and defenses on Speech Recognition - powered by SpeechBrain](https://github.com/RaphaelOlivier/robust_speech)
* [robust speech](https://github.com/RaphaelOlivier/robust_speech)

Tutorials on FGSM attack:
* (https://www.tensorflow.org/tutorials/generative/adversarial_fgsm#fast_gradient_sign_method)
* (https://pytorch.org/tutorials/beginner/fgsm_tutorial.html)