Zobrazeno 1 - 10
of 492
pro vyhledávání: '"Barni Mauro"'
The rapid proliferation of deep neural networks (DNNs) is driving a surge in model watermarking technologies, as the trained deep models themselves serve as intellectual properties. The core of existing model watermarking techniques involves modifyin
Externí odkaz:
http://arxiv.org/abs/2410.20202
Autor:
Amerini, Irene, Barni, Mauro, Battiato, Sebastiano, Bestagini, Paolo, Boato, Giulia, Bonaventura, Tania Sari, Bruni, Vittoria, Caldelli, Roberto, De Natale, Francesco, De Nicola, Rocco, Guarnera, Luca, Mandelli, Sara, Marcialis, Gian Luca, Micheletto, Marco, Montibeller, Andrea, Orru', Giulia, Ortis, Alessandro, Perazzo, Pericle, Puglisi, Giovanni, Salvi, Davide, Tubaro, Stefano, Tonti, Claudia Melis, Villari, Massimo, Vitulano, Domenico
AI-generated synthetic media, also called Deepfakes, have significantly influenced so many domains, from entertainment to cybersecurity. Generative Adversarial Networks (GANs) and Diffusion Models (DMs) are the main frameworks used to create Deepfake
Externí odkaz:
http://arxiv.org/abs/2408.00388
Synthetic image attribution addresses the problem of tracing back the origin of images produced by generative models. Extensive efforts have been made to explore unique representations of generative models and use them to attribute a synthetic image
Externí odkaz:
http://arxiv.org/abs/2405.11491
Most of the approaches proposed so far to craft targeted adversarial examples against Deep Learning classifiers are highly suboptimal and typically rely on increasing the likelihood of the target class, thus implicitly focusing on one-hot encoding se
Externí odkaz:
http://arxiv.org/abs/2401.01199
In recent years, there has been significant growth in the commercial applications of generative models, licensed and distributed by model developers to users, who in turn use them to offer services. In this scenario, there is a need to track and iden
Externí odkaz:
http://arxiv.org/abs/2311.05478
We propose a novel multi-bit box-free watermarking method for the protection of Intellectual Property Rights (IPR) of GANs with improved robustness against white-box attacks like fine-tuning, pruning, quantization, and surrogate model attacks. The wa
Externí odkaz:
http://arxiv.org/abs/2310.16919
Autor:
Barni, Mauro, Campisi, Patrizio, Delp, Edward J., Doërr, Gwenael, Fridrich, Jessica, Memon, Nasir, Pérez-González, Fernando, Rocha, Anderson, Verdoliva, Luisa, Wu, Min
Information Forensics and Security (IFS) is an active R&D area whose goal is to ensure that people use devices, data, and intellectual properties for authorized purposes and to facilitate the gathering of solid evidence to hold perpetrators accountab
Externí odkaz:
http://arxiv.org/abs/2309.12159
Despite the wide variety of methods developed for synthetic image attribution, most of them can only attribute images generated by models or architectures included in the training set and do not work with unknown architectures, hindering their applic
Externí odkaz:
http://arxiv.org/abs/2307.09822
The highly realistic image quality achieved by current image generative models has many academic and industrial applications. To limit the use of such models to benign applications, though, it is necessary that tools to conclusively detect whether an
Externí odkaz:
http://arxiv.org/abs/2305.11795