154 citations · 374 across the 25 of their papers we have counts for
26 papers
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…
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…
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…
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…
MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks
Giovanni Pasqualino, Luca Guarnera, Alessandro Ortis +1
The progress in generative models, particularly Generative Adversarial Networks (GANs), opened new possibilities for image generation but raised concerns about potential malicious…
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…
On the Exploitation of Deepfake Model Recognition
Luca Guarnera, Oliver Giudice, Matthias Niessner +1
Despite recent advances in Generative Adversarial Networks (GANs), with special focus to the Deepfake phenomenon there is no a clear understanding neither in terms of explainabilit…
Deepfake Style Transfer Mixture: a First Forensic Ballistics Study on Synthetic Images
Luca Guarnera, Oliver Giudice, Sebastiano Battiato
Most recent style-transfer techniques based on generative architectures are able to obtain synthetic multimedia contents, or commonly called deepfakes, with almost no artifacts. Re…
Fighting deepfakes by detecting GAN DCT anomalies
Oliver Giudice, Luca Guarnera, Sebastiano Battiato
To properly contrast the Deepfake phenomenon the need to design new Deepfake detection algorithms arises; the misuse of this formidable A.I. technology brings serious consequences…
Fighting Deepfake by Exposing the Convolutional Traces on Images
Luca Guarnera, Oliver Giudice, Sebastiano Battiato
Advances in Artificial Intelligence and Image Processing are changing the way people interacts with digital images and video. Widespread mobile apps like FACEAPP make use of the mo…