Luca Guarnera

Fraud is not just rarity: A causal prototype attention approach to realistic synthetic oversampling

C Giusti, Luca Guarnera, Mirko Casu, Sebastiano Battiato

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 fraud from legitimate activity. Existing research commonly attempts to address this by generating synthetic samples for the …

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$$\mu $$Flow: Leveraging Average Images for Improving Generalisation of Deepfake Faces Detectors

Orazio Pontorno, Mattia Litrico, Luca Guarnera, Mario Valerio Giuffrida et autres

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(DFF '25) 1st Deepfake Forensics Workshop: Detection, Attribution, Recognition, and Adversarial Challenges in the Era of AI-Generated Media

Sebastiano Battiato, Mirko Casu, Francesco Guarnera, Luca Guarnera et autres

The proliferation of generative models, particularly Generative Adversarial Networks (GANs) and Diffusion Models, has reshaped multimedia content creation. Alongside creative and commercial opportunities, they have introduced unprecedented risks through the production of highly realistic synthetic content, or deepfakes. These artifacts challenge visual …

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Adversarial Attacks on Deepfake Detectors: A Challenge in the Era of AI-Generated Media (AADD-2025)

Sebastiano Battiato, Mirko Casu, Francesco Guarnera, Luca Guarnera et autres

The rapid proliferation of AI-generated media, particularly hyper-realistic deepfakes, has underscored the critical need for robust detection systems to mitigate risks such as misinformation and identity theft. However, state-of-the-art deepfake detectors remain vulnerable to adversarial attacks-subtle perturbations designed to evade classification. To …

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Towards Reliable Audio Deepfake Attribution and Model Recognition: A Multi-Level Autoencoder-Based Framework

Andrea Di Pierno, Luca Guarnera, Dario Allegra, Sebastiano Battiato

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 models remains an underexplored yet crucial challenge. In this paper we introduce LAVA (Layered Architecture for Voice …

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Deep Learning for Smart Surveillance: Multi-Class Detection of People, Weapons, and Masks using YOLOv11

Ludovica Beritelli, Roberta Avanzato, Luca Guarnera, Francesco Beritelli et autres

Computer vision technologies are revolutionizing the field of security, providing intelligent and automated tools for crime prevention. In this study, we propose an advanced approach for the simultaneous detection of people, weapons, and facial masks in images extracted from surveillance camera video …

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DeepFeatureX-SN: Generalization of deepfake detection via contrastive learning

Orazio Pontorno, Luca Guarnera, Sebastiano Battiato

Abstract The rapid advancement of generative artificial intelligence, particularly in the domains of Generative Adversarial Networks (GANs) and Diffusion Models (DMs), has led to the creation of increasingly sophisticated deepfakes. These synthetic images pose significant challenges for detection systems and present growing …

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End-to-end Audio Deepfake Detection from RAW Waveforms: a RawNet-Based Approach with Cross-Dataset Evaluation

Andrea Di Pierno, Luca Guarnera, Dario Allegra, Sebastiano Battiato

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. Detecting such manipulations is especially challenging under open-world conditions, where spoofing methods encountered during testing may differ …

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WILD: a new in-the-Wild Image Linkage Dataset for synthetic image attribution

Pietro Bongini, Sara Mandelli, Andrea Montibeller, Mirko Casu et autres

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 generative techniques, as well as the scarcity of high-quality open source datasets of diverse nature for …

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Deepfake Forensic Analysis: Source Dataset Attribution and Legal Implications of Synthetic Media Manipulation

Massimiliano Cassia, Luca Guarnera, Mirko Casu, Ignazio Zangara et autres

Synthetic media generated by Generative Adversarial Networks (GANs) pose significant challenges in verifying authenticity and tracing dataset origins, raising critical concerns in copyright enforcement, privacy protection, and legal compliance. This paper introduces a novel forensic framework for identifying the training dataset (e.g., …

End-to-end Audio Deepfake Detection from RAW Waveforms: a RawNet-Based Approach with Cross-Dataset Evaluation

Andrea Di Pierno, Luca Guarnera, Dario Allegra, Sebastiano Battiato

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. Detecting such manipulations is especially challenging under open-world conditions, where spoofing methods encountered during testing may differ …

it (code pays fourni par la source)

WILD: a new in-the-Wild Image Linkage Dataset for synthetic image attribution

Pietro Bongini, Sara Mandelli, Andrea Montibeller, Mirko Casu et autres

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 generative techniques, as well as the scarcity of high-quality open source datasets of diverse nature for …