collaborators

15 papers

cs.SD2026

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…

cs.CV2026

CUPID: Reconstructing UV Texture Maps for Interpretable Person-of-Interest Deepfake Detection

Giovanni Affatato, Sara Mandelli, Edoardo Daniele Cannas +2

Deepfakes targeting a high-profile individual, known as Person-of-Interest (POI), are a threat to modern democracies and societies. Current POI deepfake detection methods still str…

cs.SD2026

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…

eess.AS2026

Mitigating data replication in text-to-audio generative diffusion models through anti-memorization guidance

Francisco Messina, Francesca Ronchini, Luca Comanducci +2

A persistent challenge in generative audio models is data replication, where the model unintentionally generates parts of its training data during inference. In this work, we addre…

cs.SD2026

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…

cs.LG2025

Enhanced Water Leak Detection with Convolutional Neural Networks and One-Class Support Vector Machine

Daniele Ugo Leonzio, Paolo Bestagini, Marco Marcon +1

Water is a critical resource that must be managed efficiently. However, a substantial amount of water is lost each year due to leaks in Water Distribution Networks (WDNs). This und…