Zobrazeno 1 - 10
of 2 032
pro vyhledávání: '"Panariello, A."'
We introduce 2D-Malafide, a novel and lightweight adversarial attack designed to deceive face deepfake detection systems. Building upon the concept of 1D convolutional perturbations explored in the speech domain, our method leverages 2D convolutional
Externí odkaz:
http://arxiv.org/abs/2408.14143
Autor:
Todisco, Massimiliano, Panariello, Michele, Wang, Xin, Delgado, Héctor, Lee, Kong Aik, Evans, Nicholas
We present Malacopula, a neural-based generalised Hammerstein model designed to introduce adversarial perturbations to spoofed speech utterances so that they better deceive automatic speaker verification (ASV) systems. Using non-linear processes to m
Externí odkaz:
http://arxiv.org/abs/2408.09300
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 sanitised, other attributes such as the spoken content should be pres
Externí odkaz:
http://arxiv.org/abs/2408.04306
Autor:
Frascaroli, Emanuele, Panariello, Aniello, Buzzega, Pietro, Bonicelli, Lorenzo, Porrello, Angelo, Calderara, Simone
With the emergence of Transformers and Vision-Language Models (VLMs) such as CLIP, fine-tuning large pre-trained models has recently become a prevalent strategy in Continual Learning. This has led to the development of numerous prompting strategies t
Externí odkaz:
http://arxiv.org/abs/2407.15793
Autor:
Panariello, Michele, Tomashenko, Natalia, Wang, Xin, Miao, Xiaoxiao, Champion, Pierre, Nourtel, Hubert, Todisco, Massimiliano, Evans, Nicholas, Vincent, Emmanuel, Yamagishi, Junichi
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 second edition held in 2022. We describe the voice anonymisation task and d
Externí odkaz:
http://arxiv.org/abs/2407.11516
Autor:
Mosconi, Matteo, Sorokin, Andriy, Panariello, Aniello, Porrello, Angelo, Bonato, Jacopo, Cotogni, Marco, Sabetta, Luigi, Calderara, Simone, Cucchiara, Rita
The use of skeletal data allows deep learning models to perform action recognition efficiently and effectively. Herein, we believe that exploring this problem within the context of Continual Learning is crucial. While numerous studies focus on skelet
Externí odkaz:
http://arxiv.org/abs/2407.01397
Autor:
Tomashenko, Natalia, Miao, Xiaoxiao, Champion, Pierre, Meyer, Sarina, Wang, Xin, Vincent, Emmanuel, Panariello, Michele, Evans, Nicholas, Yamagishi, Junichi, Todisco, Massimiliano
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 states. The organizers provide development and evaluation datasets and
Externí odkaz:
http://arxiv.org/abs/2404.02677
Autor:
Panariello, Aniello, Mancusi, Gianluca, Ali, Fedy Haj, Porrello, Angelo, Calderara, Simone, Cucchiara, Rita
Accurate per-object distance estimation is crucial in safety-critical applications such as autonomous driving, surveillance, and robotics. Existing approaches rely on two scales: local information (i.e., the bounding box proportions) or global inform
Externí odkaz:
http://arxiv.org/abs/2401.03191
The vast majority of approaches to speaker anonymization involve the extraction of fundamental frequency estimates, linguistic features and a speaker embedding which is perturbed to obfuscate the speaker identity before an anonymized speech waveform
Externí odkaz:
http://arxiv.org/abs/2309.14129
Autor:
Chouchane, Oubaida, Panariello, Michele, Galdi, Chiara, Todisco, Massimiliano, Evans, Nicholas
This study investigates the impact of gender information on utility, privacy, and fairness in voice biometric systems, guided by the General Data Protection Regulation (GDPR) mandates, which underscore the need for minimizing the processing and stora
Externí odkaz:
http://arxiv.org/abs/2308.14049