10 citations · 11 across the 16 of their papers we have counts for
18 papers
Preemptive defense against re-identification attacks on voice anonymization via adversarial perturbation
Michele Panariello, Yibo Bai, Massimiliano Todisco +1
Voice anonymization (VA) is used to conceal the voice identities in speech data. Performance is usually estimated using automatic speaker verification (ASV) as a proxy to judge the…
Latent Secret Spin: Keyed Orthogonal Rotations for Blind Speech Watermarking in Anisotropic Latent Spaces
Emma Coletta, Massimiliano Todisco, Michele Panariello +2
We introduce Latent Secret Spin (LSS), a blind speech watermarking method based on geometric operations in codec latent space. Based upon orthogonal rotations to principal componen…
Evaluating voice anonymisation using similarity rank disclosure
Shilpa Chandra, Matteo Pettenò, Nicholas Evans +7
The evaluation of voice anonymisation remains challenging. Current practice relies on automatic speaker verification metrics such as the equal error rate (EER). Performance estimat…
The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymization
Natalia Tomashenko, Xiaoxiao Miao, Pierre Champion +7
We present results and analyses from the third VoicePrivacy Challenge held in 2024, which focuses on advancing voice anonymization technologies. The task was to develop a voice ano…
MDD: a Mask Diffusion Detector to Protect Speaker Verification Systems from Adversarial Perturbations
Yibo Bai, Sizhou Chen, Michele Panariello +3
Speaker verification systems are increasingly deployed in security-sensitive applications but remain highly vulnerable to adversarial perturbations. In this work, we propose the Ma…
Reference-free Adversarial Sex Obfuscation in Speech
Yangyang Qu, Michele Panariello, Massimiliano Todisco +1
Sex conversion in speech involves privacy risks from data collection and often leaves residual sex-specific cues in outputs, even when target speaker references are unavailable. We…
The Risks and Detection of Overestimated Privacy Protection in Voice Anonymisation
Michele Panariello, Sarina Meyer, Pierre Champion +4
Voice anonymisation aims to conceal the voice identity of speakers in speech recordings. Privacy protection is usually estimated from the difficulty of using a speaker verification…
2D-Malafide: Adversarial Attacks Against Face Deepfake Detection Systems
Chiara Galdi, Michele Panariello, Massimiliano Todisco +1
We introduce 2D-Malafide, a novel and lightweight adversarial attack designed to deceive face deepfake detection systems. Building upon the concept of 1D convolutional perturbation…
Malacopula: adversarial automatic speaker verification attacks using a neural-based generalised Hammerstein model
Massimiliano Todisco, Michele Panariello, Xin Wang +3
We present Malacopula, a neural-based generalised Hammerstein model designed to introduce adversarial perturbations to spoofed speech utterances so that they better deceive automat…
Preserving spoken content in voice anonymisation with character-level vocoder conditioning
Michele Panariello, Massimiliano Todisco, Nicholas Evans
Voice anonymisation can be used to help protect speaker privacy when speech data is shared with untrusted others. In most practical applications, while the voice identity should be…
The VoicePrivacy 2022 Challenge: Progress and Perspectives in Voice Anonymisation
Michele Panariello, Natalia Tomashenko, Xin Wang +7
The VoicePrivacy Challenge promotes the development of voice anonymisation solutions for speech technology. In this paper we present a systematic overview and analysis of the secon…
Spoof Diarization: "What Spoofed When" in Partially Spoofed Audio
Lin Zhang, Xin Wang, Erica Cooper +4
This paper defines Spoof Diarization as a novel task in the Partial Spoof (PS) scenario. It aims to determine what spoofed when, which includes not only locating spoof regions but…
A Comparison of Differential Performance Metrics for the Evaluation of Automatic Speaker Verification Fairness
Oubaida Chouchane, Christoph Busch, Chiara Galdi +2
When decisions are made and when personal data is treated by automated processes, there is an expectation of fairness -- that members of different demographic groups receive equita…
The VoicePrivacy 2024 Challenge Evaluation Plan
Natalia Tomashenko, Xiaoxiao Miao, Pierre Champion +7
The task of the challenge is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content and emotional s…
SynVox2: Towards a privacy-friendly VoxCeleb2 dataset
Xiaoxiao Miao, Xin Wang, Erica Cooper +5
The success of deep learning in speaker recognition relies heavily on the use of large datasets. However, the data-hungry nature of deep learning methods has already being question…
Speaker anonymization using neural audio codec language models
Michele Panariello, Francesco Nespoli, Massimiliano Todisco +1
The vast majority of approaches to speaker anonymization involve the extraction of fundamental frequency estimates, linguistic features and a speaker embedding which is perturbed t…
Spoofing attack augmentation: can differently-trained attack models improve generalisation?
Wanying Ge, Xin Wang, Junichi Yamagishi +2
A reliable deepfake detector or spoofing countermeasure (CM) should be robust in the face of unpredictable spoofing attacks. To encourage the learning of more generaliseable artefa…
A False Sense of Privacy: Towards a Reliable Evaluation Methodology for the Anonymization of Biometric Data
Simon Hanisch, Julian Todt, Jose Patino +2
Biometric data contains distinctive human traits such as facial features or gait patterns. The use of biometric data permits an individuation so exact that the data is utilized eff…