Salvatore Sambataro

I am a PhD student in Artificial Intelligence and Computer Vision, focusing on Generative AI, Video Understanding, and Multimedia Forensics. My research explores how to analyze and interpret complex visual content from videos, as well as the capabilities, limitations, and societal impact of modern generative models and synthetic media. I design methods for video understanding that combine deep learning, representation learning, and multimodal reasoning to extract meaningful information from dynamic visual data.

Alongside my academic work, I am an AI Engineer and Researcher at iCTLab s.r.l., where I develop and deploy AI solutions across a range of domains. I primarily work on computer vision projects, but I also contribute to systems involving time series analysis, natural language processing, and retrieval-augmented generation (RAG). In this role, I bridge research and production: I design models, build scalable pipelines, and translate cutting-edge ideas into real-world applications. I am also actively involved in research activities within the company, contributing to experimental prototypes, publications, and collaborative innovation.

selected publications

SynthForensics: A Multi-Generator Benchmark for Detecting Synthetic Video Deepfakes

Roberto Leotta^† Salvatore Alfio Sambataro^†, and 6 more authors

In arXiv Preprint, 2026

† Joint first authorship

Abs PDF

The landscape of synthetic media has been irrevocably altered by text-to-video (T2V) models, whose outputs are rapidly approaching indistinguishability from reality. Critically, this technology is no longer confined to large-scale labs; the proliferation of efficient, open-source generators is democratizing the ability to create high-fidelity synthetic content on consumer-grade hardware. This makes existing face-centric and manipulation-based benchmarks obsolete. To address this urgent threat, we introduce SynthForensics, to the best of our knowledge the first human-centric benchmark for detecting purely synthetic video deepfakes. The benchmark comprises 6,815 unique videos from five architecturally distinct, state-of-the-art open-source T2V models. Its construction was underpinned by a meticulous two-stage, human-in-the-loop validation to ensure high semantic and visual quality. Each video is provided in four versions (raw, lossless, light, and heavy compression) to enable real-world robustness testing. Experiments demonstrate that state-of-the-art detectors are both fragile and exhibit limited generalization when evaluated on this new domain: we observe a mean performance drop of 29.19% AUC, with some methods performing worse than random chance, and top models losing over 30 points under heavy compression. The paper further investigates the efficacy of training on SynthForensics as a means to mitigate these observed performance gaps, achieving robust generalization to unseen generators (93.81% AUC), though at the cost of reduced backward compatibility with traditional manipulation-based deepfakes. The complete dataset and all generation metadata, including the specific prompts and inference parameters for every video, will be made publicly available.