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Auditing Patient Privacy in Medical Generative Models: Scalable Memorization Detection with DeepSSIM++
Antonio Scardace, Francesco Guarnera, Sebastiano Battiato +1
While deep generative models offer new opportunities for medical image synthesis and data sharing, their ability to memorize and reproduce training samples raises serious concerns…
The K-Space Signature: Frequency-Domain Representation Learning for Medical Deepfake Detection
Riccardo Raciti, Francesco Guarnera, Francesco Rundo +2
In medical imaging, generative models are increasingly deployed to synthesize realistic data and augment limited datasets. Unfortunately, while beneficial for privacy-preserving da…
SynthForensics: Benchmarking and Evaluating People-Centric Synthetic Video Deepfakes
Roberto Leotta, Salvatore Alfio Sambataro, Claudio Vittorio Ragaglia +5
Modern T2V/I2V generators synthesize people increasingly hard to distinguish from authentic footage, while current evaluation suites lag: legacy benchmarks target manipulation-base…
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…
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…