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20162026
most citedFighting Deepfake by Exposing the Convolutional Traces on Images

154 citations · 780 across the 51 of their papers we have counts for

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Showing 2026Show all

6 papers · 1 filter

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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-…

eess.IV2026

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…

cs.CV2026

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…