3 citations · 7 across the 11 of their papers we have counts for
13 papers
Synthetic Speech Attribution via Prototypical Networks
Viola Negroni, Paolo Bestagini, Stefano Tubaro
Synthetic speech attribution aims to identify the generative system responsible for a speech signal, but current approaches typically rely on black-box neural networks that provide…
Toward Interpretable Speech Deepfake Detection using Artifact-Specific Experts and Calibrated Detection Scores
Viola Negroni, Xin Wang, Wanying Ge +3
In this work, we propose an interpretable framework for speech deepfake detection based on artifact-specific expert models. Rather than relying on black-box decisions, the framewor…
Multi-Task Transformer for Explainable Speech Deepfake Detection via Formant Modeling
Viola Negroni, Luca Cuccovillo, Paolo Bestagini +2
In this work, we introduce a multi-task transformer for speech deepfake detection, capable of predicting formant trajectories and voicing patterns over time, ultimately classifying…
Forensic Similarity for Speech Deepfakes
Viola Negroni, Davide Salvi, Daniele Ugo Leonzio +2
In this paper, we introduce the concept of forensic similarity in the speech deepfake detection domain, which aims to determine whether two audio segments share the same underlying…
Attention-based Mixture of Experts for Robust Speech Deepfake Detection
Viola Negroni, Davide Salvi, Alessandro Ilic Mezza +2
AI-generated speech is becoming increasingly used in everyday life, powering virtual assistants, accessibility tools, and other applications. However, it is also being exploited fo…
Phoneme-Level Analysis for Person-of-Interest Speech Deepfake Detection
Davide Salvi, Viola Negroni, Sara Mandelli +2
Recent advances in generative AI have made the creation of speech deepfakes widely accessible, posing serious challenges to digital trust. To counter this, various speech deepfake…
Source Verification for Speech Deepfakes
Viola Negroni, Davide Salvi, Paolo Bestagini +1
With the proliferation of speech deepfake generators, it becomes crucial not only to assess the authenticity of synthetic audio but also to trace its origin. While source attributi…
Anomaly Detection and Localization for Speech Deepfakes via Feature Pyramid Matching
Emma Coletta, Davide Salvi, Viola Negroni +2
The rise of AI-driven generative models has enabled the creation of highly realistic speech deepfakes - synthetic audio signals that can imitate target speakers' voices - raising c…
Comparative Analysis of ASR Methods for Speech Deepfake Detection
Davide Salvi, Amit Kumar Singh Yadav, Kratika Bhagtani +3
Recent techniques for speech deepfake detection often rely on pre-trained self-supervised models. These systems, initially developed for Automatic Speech Recognition (ASR), have pr…
Freeze and Learn: Continual Learning with Selective Freezing for Speech Deepfake Detection
Davide Salvi, Viola Negroni, Luca Bondi +2
In speech deepfake detection, one of the critical aspects is developing detectors able to generalize on unseen data and distinguish fake signals across different datasets. Common a…
Leveraging Mixture of Experts for Improved Speech Deepfake Detection
Viola Negroni, Davide Salvi, Alessandro Ilic Mezza +2
Speech deepfakes pose a significant threat to personal security and content authenticity. Several detectors have been proposed in the literature, and one of the primary challenges…
Localization of Synthetic Manipulations in Western Blot Images
Anmol Manjunath, Viola Negroni, Sara Mandelli +2
Recent breakthroughs in deep learning and generative systems have significantly fostered the creation of synthetic media, as well as the local alteration of real content via the in…
Analyzing the Impact of Splicing Artifacts in Partially Fake Speech Signals
Viola Negroni, Davide Salvi, Paolo Bestagini +1
Speech deepfake detection has recently gained significant attention within the multimedia forensics community. Related issues have also been explored, such as the identification of…