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pro vyhledávání: '"COZZOLINO A"'
Autor:
Bologna C, Cozzolino A, Ferraro A, Guerra M, Guida A, Lugarà M, Coppola MG, Tirelli P, Sicignano M, Madonna P, Di Micco P
Publikováno v:
Journal of Blood Medicine, Vol Volume 13, Pp 167-170 (2022)
Carolina Bologna,1 Antonio Cozzolino,1 Andrea Ferraro,1 MariaVittoria Guerra,1 Anna Guida,1 Marina Lugarà,1 Maria Gabriella Coppola,1 Paolo Tirelli,1 Marilena Sicignano,1 Pasquale Madonna,1 Pierpaolo Di Micco2 1UOC Medicina Generale Ospedale del Mar
Externí odkaz:
https://doaj.org/article/b2ab3824823c48499bba0bff4e763cb9
Detecting AI-generated images has become an extraordinarily difficult challenge as new generative architectures emerge on a daily basis with more and more capabilities and unprecedented realism. New versions of many commercial tools, such as DALLE, M
Externí odkaz:
http://arxiv.org/abs/2409.15875
In recent years, many forensic detectors have been proposed to detect AI-generated images and prevent their use for malicious purposes. Convolutional neural networks (CNNs) have long been the dominant architecture in this field and have been the subj
Externí odkaz:
http://arxiv.org/abs/2407.19553
Generalization is a main issue for current audio deepfake detectors, which struggle to provide reliable results on out-of-distribution data. Given the speed at which more and more accurate synthesis methods are developed, it is very important to desi
Externí odkaz:
http://arxiv.org/abs/2405.02179
Autor:
Tariang, Diangarti, Corvi, Riccardo, Cozzolino, Davide, Poggi, Giovanni, Nagano, Koki, Verdoliva, Luisa
In this work we present an overview of approaches for the detection and attribution of synthetic images and highlight their strengths and weaknesses. We also point out and discuss hot topics in this field and outline promising directions for future r
Externí odkaz:
http://arxiv.org/abs/2405.00196
The aim of this work is to explore the potential of pre-trained vision-language models (VLMs) for universal detection of AI-generated images. We develop a lightweight detection strategy based on CLIP features and study its performance in a wide varie
Externí odkaz:
http://arxiv.org/abs/2312.00195
Autor:
Veselovsky, Veniamin, Ribeiro, Manoel Horta, Cozzolino, Philip, Gordon, Andrew, Rothschild, David, West, Robert
We show that the use of large language models (LLMs) is prevalent among crowd workers, and that targeted mitigation strategies can significantly reduce, but not eliminate, LLM use. On a text summarization task where workers were not directed in any w
Externí odkaz:
http://arxiv.org/abs/2310.15683
The Video and Image Processing (VIP) Cup is a student competition that takes place each year at the IEEE International Conference on Image Processing. The 2022 IEEE VIP Cup asked undergraduate students to develop a system capable of distinguishing pr
Externí odkaz:
http://arxiv.org/abs/2309.12428
The ability to detect manipulated visual content is becoming increasingly important in many application fields, given the rapid advances in image synthesis methods. Of particular concern is the possibility of modifying the content of medical images,
Externí odkaz:
http://arxiv.org/abs/2309.07973
Autor:
Mazzitelli Maria, Alberto Enrico Maraolo, Claudia Cozzolino, Lolita Sasset, Anna Ferrari, Monica Basso, Eleonora Vania, Nicola Bonadiman, Vincenzo Scaglione, Anna Maria Cattelan
Publikováno v:
European Journal of Medical Research, Vol 29, Iss 1, Pp 1-9 (2024)
Abstract Background The potential efficacy of early combination therapy, based on an antiviral plus a monoclonal antibody, for COVID-19 in severely immunocompromised patients is matter of debate. Objectives Our aim was to describe the impact on clini
Externí odkaz:
https://doaj.org/article/cf95a974b9d446789b1fbf7b1550e541