154 citations · 780 across the 51 of their papers we have counts for
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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…
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-…
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…
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…
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…
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…
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…
Uncertainty-guided Open-Set Source-Free Unsupervised Domain Adaptation with Target-private Class Segregation
Mattia Litrico, Davide Talon, Sebastiano Battiato +3
Standard Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target but usually requires simultaneous access to both source…
On the exploitation of DCT statistics for cropping detectors
Claudio Vittorio Ragaglia, Francesco Guarnera, Sebastiano Battiato
{The study of frequency components derived from Discrete Cosine Transform (DCT) has been widely used in image analysis. In recent years it has been observed that significant inform…
On the Exploitation of DCT-Traces in the Generative-AI Domain
Orazio Pontorno, Luca Guarnera, Sebastiano Battiato
Deepfakes represent one of the toughest challenges in the world of Cybersecurity and Digital Forensics, especially considering the high-quality results obtained with recent generat…
A Novel Dataset for Non-Destructive Inspection of Handwritten Documents
Eleonora Breci, Luca Guarnera, Sebastiano Battiato
Forensic handwriting examination is a branch of Forensic Science that aims to examine handwritten documents in order to properly define or hypothesize the manuscript's author. Thes…
GenAI Mirage: The Impostor Bias and the Deepfake Detection Challenge in the Era of Artificial Illusions
Mirko Casu, Luca Guarnera, Pasquale Caponnetto +1
This paper examines the impact of cognitive biases on decision-making in forensics and digital forensics, exploring biases such as confirmation bias, anchoring bias, and hindsight…
Innovative Methods for Non-Destructive Inspection of Handwritten Documents
Eleonora Breci, Luca Guarnera, Sebastiano Battiato
Handwritten document analysis is an area of forensic science, with the goal of establishing authorship of documents through examination of inherent characteristics. Law enforcement…
Improving Video Deepfake Detection: A DCT-Based Approach with Patch-Level Analysis
Luca Guarnera, Salvatore Manganello, Sebastiano Battiato
A new algorithm for the detection of deepfakes in digital videos is presented. The I-frames were extracted in order to provide faster computation and analysis than approaches descr…
Not with my name! Inferring artists' names of input strings employed by Diffusion Models
Roberto Leotta, Oliver Giudice, Luca Guarnera +1
Diffusion Models (DM) are highly effective at generating realistic, high-quality images. However, these models lack creativity and merely compose outputs based on their training da…
Level Up the Deepfake Detection: a Method to Effectively Discriminate Images Generated by GAN Architectures and Diffusion Models
Luca Guarnera, Oliver Giudice, Sebastiano Battiato
The image deepfake detection task has been greatly addressed by the scientific community to discriminate real images from those generated by Artificial Intelligence (AI) models: a…