154 citations · 780 across the 51 of their papers we have counts for
11 papers · 1 filter
Proto-LeakNet: Towards Signal-Leak Aware Attribution in Synthetic Human Face Imagery
Claudio Giusti, Luca Guarnera, Sebastiano Battiato
The growing sophistication of synthetic image and deepfake generation models has turned source attribution and authenticity verification into a critical challenge for modern comput…
Towards Reliable Audio Deepfake Attribution and Model Recognition: A Multi-Level Autoencoder-Based Framework
Andrea Di Pierno, Luca Guarnera, Dario Allegra +1
The proliferation of audio deepfakes poses a growing threat to trust in digital communications. While detection methods have advanced, attributing audio deepfakes to their source m…
Count2Density: Crowd Density Estimation without Location-level Annotations
Mattia Litrico, Feng Chen, Michael Pound +3
Crowd density estimation is a well-known computer vision task aimed at estimating the density distribution of people in an image. The main challenge in this domain is the reliance…
Temporally-Aware Diffusion Model for Brain Progression Modelling with Bidirectional Temporal Regularisation
Mattia Litrico, Francesco Guarnera, Mario Valerio Giuffrida +2
Generating realistic MRIs to accurately predict future changes in the structure of brain is an invaluable tool for clinicians in assessing clinical outcomes and analysing the disea…
A Novel Metric for Detecting Memorization in Generative Models for Brain MRI Synthesis
Antonio Scardace, Lemuel Puglisi, Francesco Guarnera +2
Deep generative models have emerged as a transformative tool in medical imaging, offering substantial potential for synthetic data generation. However, recent empirical studies hig…
TRUST: Leveraging Text Robustness for Unsupervised Domain Adaptation
Mattia Litrico, Mario Valerio Giuffrida, Sebastiano Battiato +1
Recent unsupervised domain adaptation (UDA) methods have shown great success in addressing classical domain shifts (e.g., synthetic-to-real), but they still suffer under complex sh…
Benchmarking GANs, Diffusion Models, and Flow Matching for T1w-to-T2w MRI Translation
Andrea Moschetto, Lemuel Puglisi, Alec Sargood +4
Magnetic Resonance Imaging (MRI) enables the acquisition of multiple image contrasts, such as T1-weighted (T1w) and T2-weighted (T2w) scans, each offering distinct diagnostic insig…
Fraud is Not Just Rarity: A Causal Prototype Attention Approach to Realistic Synthetic Oversampling
Claudio Giusti, Luca Guarnera, Mirko Casu +1
Detecting fraudulent credit card transactions remains a significant challenge, due to the extreme class imbalance in real-world data and the often subtle patterns that separate fra…
Deepfake Forensic Analysis: Source Dataset Attribution and Legal Implications of Synthetic Media Manipulation
Massimiliano Cassia, Luca Guarnera, Mirko Casu +2
Synthetic media generated by Generative Adversarial Networks (GANs) pose significant challenges in verifying authenticity and tracing dataset origins, raising critical concerns in…
End-to-end Audio Deepfake Detection from RAW Waveforms: a RawNet-Based Approach with Cross-Dataset Evaluation
Andrea Di Pierno, Luca Guarnera, Dario Allegra +1
Audio deepfakes represent a growing threat to digital security and trust, leveraging advanced generative models to produce synthetic speech that closely mimics real human voices. D…
WILD: a new in-the-Wild Image Linkage Dataset for synthetic image attribution
Pietro Bongini, Sara Mandelli, Andrea Montibeller +14
Synthetic image source attribution is an open challenge, with an increasing number of image generators being released yearly. The complexity and the sheer number of available gener…