154 citations · 780 across the 51 of their papers we have counts for
59 papers
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…
HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes
Orazio Pontorno, Luca Guarnera, Zahid Akhtar +1
The emergence of medical deepfakes, i.e., medical images manipulated by deep generative models, poses a significant threat to clinical workflows. However, existing detectors suffer…
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…
Flow: Leveraging Average Images for Improving Generalisation of Deepfake Faces Detectors
Orazio Pontorno, Mattia Litrico, Luca Guarnera +2
Current generative models, including GANs and diffusion models, have reached an outstanding level of photorealism, posing significant risks to privacy and security. To ensure real-…
Reinforcing the Weakest Links: Modernizing SIENA with Targeted Deep Learning Integration
Riccardo Raciti, Lemuel Puglisi, Francesco Guarnera +2
Percentage Brain Volume Change (PBVC) derived from Magnetic Resonance Imaging (MRI) is a widely used biomarker of brain atrophy, with SIENA among the most established methods for i…
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…
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…
ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation -- Methods and Results
Alessia Rondinella, Francesco Guarnera, Elena Crispino +5
This report summarizes the outcomes of the ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation (MSLesSeg). The competition aimed to develop methods capable of automatic…
Enhancing Crime Scene Investigations through Virtual Reality and Deep Learning Techniques
Antonino Zappalà, Luca Guarnera, Vincenzo Rinaldi +2
The analysis of a crime scene is a pivotal activity in forensic investigations. Crime Scene Investigators and forensic science practitioners rely on best practices, standard operat…
Deepfake Media Forensics: State of the Art and Challenges Ahead
Irene Amerini, Mauro Barni, Sebastiano Battiato +21
AI-generated synthetic media, also called Deepfakes, have significantly influenced so many domains, from entertainment to cybersecurity. Generative Adversarial Networks (GANs) and…
SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions
Lemuel Puglisi, Alessia Rondinella, Linda De Meo +3
Brain age is a critical measure that reflects the biological ageing process of the brain. The gap between brain age and chronological age, referred to as brain PAD (Predicted Age D…
TADM: Temporally-Aware Diffusion Model for Neurodegenerative Progression on Brain MRI
Mattia Litrico, Francesco Guarnera, Valerio Giuffirda +2
Generating realistic images to accurately predict changes in the structure of brain MRI is a crucial tool for clinicians. Such applications help assess patients' outcomes and analy…
PhilHumans: Benchmarking Machine Learning for Personal Health
Vadim Liventsev, Vivek Kumar, Allmin Pradhap Singh Susaiyah +14
The use of machine learning in Healthcare has the potential to improve patient outcomes as well as broaden the reach and affordability of Healthcare. The history of other applicati…
DeepFeatureX Net: Deep Features eXtractors based Network for discriminating synthetic from real images
Orazio Pontorno, Luca Guarnera, Sebastiano Battiato
Deepfakes, synthetic images generated by deep learning algorithms, represent one of the biggest challenges in the field of Digital Forensics. The scientific community is working to…