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Found 6 papers, 0 papers with code
Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification
In this paper, we propose a method to detect the presence of adversarial examples, i. e., a binary classifier distinguishing between benign and adversarial examples.
Defense against Adversarial Attacks on Hybrid Speech Recognition using Joint Adversarial Fine-tuning with Denoiser
We propose three defenses--denoiser pre-processor, adversarially fine-tuning ASR model, and adversarially fine-tuning joint model of ASR and denoiser.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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AdvEst: Adversarial Perturbation Estimation to Classify and Detect Adversarial Attacks against Speaker Identification
Building on our previous work that used representation learning to classify and detect adversarial attacks, we propose an improvement to it using AdvEst, a method to estimate adversarial perturbation.
Representation Learning to Classify and Detect Adversarial Attacks against Speaker and Speech Recognition Systems
Also, representations trained to classify attacks against speaker identification can be used also to classify attacks against speaker verification and speech recognition.
Adversarial Attacks and Defenses for Speech Recognition Systems
We investigate two threat models: a denial-of-service scenario where fast gradient-sign method (FGSM) or weak projected gradient descent (PGD) attacks are used to degrade the model's word error rate (WER); and a targeted scenario where a more potent imperceptible attack forces the system to recognize a specific phrase.
Study of Pre-processing Defenses against Adversarial Attacks on State-of-the-art Speaker Recognition Systems
Such attacks pose severe security risks, making it vital to deep-dive and understand how much the state-of-the-art SR systems are vulnerable to these attacks.
