Attacking Speaker Recognition With Deep Generative Models
In this paper we investigate the ability of generative adversarial networks (GANs) to synthesize spoofing attacks on modern speaker recognition systems. We first show that samples generated with SampleRNN and WaveNet are unable to fool a CNN-based speaker recognition system. We propose a modification of the Wasserstein GAN objective function to make use of data that is real but not from the class being learned. Our semi-supervised learning method is able to perform both targeted and untargeted attacks, raising questions related to security in speaker authentication systems.
Code (0)
등록된 구현이 없습니다.
Tasks
Speaker RecognitionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Universal Adversarial Perturbations Generative Network for Speaker Recognition
Attacking deep learning based biometric systems has drawn more and more attention with the wide deployment of fingerprint/face/speaker recognition systems, given the fact that the neural networks are vulnerable to the ad…
Speaker RecognitionThe Database and Benchmark for the Source Speaker Tracing Challenge 2024
Voice conversion (VC) systems can transform audio to mimic another speaker's voice, thereby attacking speaker verification (SV) systems. However, ongoing studies on source speaker verification (SSV) are hindered by limit…
Multi-Task LearningSpeaker VerificationVoice ConversionNPLDA: A Deep Neural PLDA Model for Speaker Verification
The state-of-art approach for speaker verification consists of a neural network based embedding extractor along with a backend generative model such as the Probabilistic Linear Discriminant Analysis (PLDA). In this work,…
Speaker RecognitionSpeaker VerificationLarge Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition
Given recent advances in generative AI technology, a key question is how large language models (LLMs) can enhance acoustic modeling tasks using text decoding results from a frozen, pretrained automatic speech recognition…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Emotion RecognitionLanguage Modeling+4Interpretable Spectrum Transformation Attacks to Speaker Recognition
The success of adversarial attacks to speaker recognition is mainly in white-box scenarios. When applying the adversarial voices that are generated by attacking white-box surrogate models to black-box victim models, i.e.…
Speaker Recognition