Zobrazeno 1 - 10
of 175
pro vyhledávání: '"D'Antonio, Emanuele"'
With the widespread use of social media, organizations, and individuals use these platforms to raise funds and support causes. Unfortunately, this has led to the rise of scammers in soliciting fraudulent donations. In this study, we conduct a large-s
Externí odkaz:
http://arxiv.org/abs/2412.15621
Autor:
Villani, Francesco, Maljkovic, Igor, Lazzaro, Dario, Sotgiu, Angelo, Cinà, Antonio Emanuele, Roli, Fabio
Deep Neural Networks are vulnerable to adversarial examples, i.e., carefully crafted input samples that can cause models to make incorrect predictions with high confidence. To mitigate these vulnerabilities, adversarial training and detection-based d
Externí odkaz:
http://arxiv.org/abs/2412.10353
Autor:
Ledda, Emanuele, Scodeller, Giovanni, Angioni, Daniele, Piras, Giorgio, Cinà, Antonio Emanuele, Fumera, Giorgio, Biggio, Battista, Roli, Fabio
In learning problems, the noise inherent to the task at hand hinders the possibility to infer without a certain degree of uncertainty. Quantifying this uncertainty, regardless of its wide use, assumes high relevance for security-sensitive application
Externí odkaz:
http://arxiv.org/abs/2410.21952
Autor:
Villani, Francesco, Lazzaro, Dario, Cinà, Antonio Emanuele, Dell'Amico, Matteo, Biggio, Battista, Roli, Fabio
Data poisoning attacks on clustering algorithms have received limited attention, with existing methods struggling to scale efficiently as dataset sizes and feature counts increase. These attacks typically require re-clustering the entire dataset mult
Externí odkaz:
http://arxiv.org/abs/2408.07558
Despite explainable AI (XAI) has recently become a hot topic and several different approaches have been developed, there is still a widespread belief that it lacks a convincing unifying foundation. On the other hand, over the past centuries, the very
Externí odkaz:
http://arxiv.org/abs/2407.18782
Autor:
Chen, Zhang, Demetrio, Luca, Gupta, Srishti, Feng, Xiaoyi, Xia, Zhaoqiang, Cinà, Antonio Emanuele, Pintor, Maura, Oneto, Luca, Demontis, Ambra, Biggio, Battista, Roli, Fabio
Thanks to their extensive capacity, over-parameterized neural networks exhibit superior predictive capabilities and generalization. However, having a large parameter space is considered one of the main suspects of the neural networks' vulnerability t
Externí odkaz:
http://arxiv.org/abs/2406.10090
Autor:
Cinà, Antonio Emanuele, Rony, Jérôme, Pintor, Maura, Demetrio, Luca, Demontis, Ambra, Biggio, Battista, Ayed, Ismail Ben, Roli, Fabio
Adversarial examples are typically optimized with gradient-based attacks. While novel attacks are continuously proposed, each is shown to outperform its predecessors using different experimental setups, hyperparameter settings, and number of forward
Externí odkaz:
http://arxiv.org/abs/2404.19460
Autor:
Cinà, Antonio Emanuele, Villani, Francesco, Pintor, Maura, Schönherr, Lea, Biggio, Battista, Pelillo, Marcello
Evaluating the adversarial robustness of deep networks to gradient-based attacks is challenging. While most attacks consider $\ell_2$- and $\ell_\infty$-norm constraints to craft input perturbations, only a few investigate sparse $\ell_1$- and $\ell_
Externí odkaz:
http://arxiv.org/abs/2402.01879
Autor:
Zheng, Yang, Demetrio, Luca, Cinà, Antonio Emanuele, Feng, Xiaoyi, Xia, Zhaoqiang, Jiang, Xiaoyue, Demontis, Ambra, Biggio, Battista, Roli, Fabio
RGB-D object recognition systems improve their predictive performances by fusing color and depth information, outperforming neural network architectures that rely solely on colors. While RGB-D systems are expected to be more robust to adversarial exa
Externí odkaz:
http://arxiv.org/abs/2309.07106
Autor:
Lazzaro, Dario, Cinà, Antonio Emanuele, Pintor, Maura, Demontis, Ambra, Biggio, Battista, Roli, Fabio, Pelillo, Marcello
Deep learning models undergo a significant increase in the number of parameters they possess, leading to the execution of a larger number of operations during inference. This expansion significantly contributes to higher energy consumption and predic
Externí odkaz:
http://arxiv.org/abs/2307.00368