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pro vyhledávání: '"Grosse, Kathrin"'
Autor:
Llorca, David Fernández, Hamon, Ronan, Junklewitz, Henrik, Grosse, Kathrin, Kunze, Lars, Seiniger, Patrick, Swaim, Robert, Reed, Nick, Alahi, Alexandre, Gómez, Emilia, Sánchez, Ignacio, Kriston, Akos
This study explores the complexities of integrating Artificial Intelligence (AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI components and the impact on testing procedures, focusing on some of the essential requirements
Externí odkaz:
http://arxiv.org/abs/2403.14641
Autor:
Messaoud, Kaouther, Grosse, Kathrin, Chen, Mickael, Cord, Matthieu, Pérez, Patrick, Alahi, Alexandre
Autonomous vehicles ought to predict the surrounding agents' trajectories to allow safe maneuvers in uncertain and complex traffic situations. As companies increasingly apply trajectory prediction in the real world, security becomes a relevant concer
Externí odkaz:
http://arxiv.org/abs/2312.13863
Recent works have identified a gap between research and practice in artificial intelligence security: threats studied in academia do not always reflect the practical use and security risks of AI. For example, while models are often studied in isolati
Externí odkaz:
http://arxiv.org/abs/2311.09994
Autor:
Demontis, Ambra, Pintor, Maura, Demetrio, Luca, Grosse, Kathrin, Lin, Hsiao-Ying, Fang, Chengfang, Biggio, Battista, Roli, Fabio
Reinforcement learning allows machines to learn from their own experience. Nowadays, it is used in safety-critical applications, such as autonomous driving, despite being vulnerable to attacks carefully crafted to either prevent that the reinforcemen
Externí odkaz:
http://arxiv.org/abs/2212.06123
Autor:
Grosse, Kathrin, Bieringer, Lukas, Besold, Tarek Richard, Biggio, Battista, Krombholz, Katharina
Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild. In this paper, we report on a quantitative study with 139 industrial practitioners. We anal
Externí odkaz:
http://arxiv.org/abs/2207.05164
Autor:
Cinà, Antonio Emanuele, Grosse, Kathrin, Demontis, Ambra, Vascon, Sebastiano, Zellinger, Werner, Moser, Bernhard A., Oprea, Alina, Biggio, Battista, Pelillo, Marcello, Roli, Fabio
The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it is sufficiently representative of the dat
Externí odkaz:
http://arxiv.org/abs/2205.01992
Autor:
Cinà, Antonio Emanuele, Grosse, Kathrin, Demontis, Ambra, Biggio, Battista, Roli, Fabio, Pelillo, Marcello
The recent success of machine learning (ML) has been fueled by the increasing availability of computing power and large amounts of data in many different applications. However, the trustworthiness of the resulting models can be compromised when such
Externí odkaz:
http://arxiv.org/abs/2204.05986
Autor:
Grosse, Kathrin, Alahi, Alexandre
Publikováno v:
In Transportation Research Part C December 2024 169
Autor:
Cinà, Antonio Emanuele, Grosse, Kathrin, Vascon, Sebastiano, Demontis, Ambra, Biggio, Battista, Roli, Fabio, Pelillo, Marcello
Backdoor attacks inject poisoning samples during training, with the goal of forcing a machine learning model to output an attacker-chosen class when presented a specific trigger at test time. Although backdoor attacks have been demonstrated in a vari
Externí odkaz:
http://arxiv.org/abs/2106.07214
Although machine learning is widely used in practice, little is known about practitioners' understanding of potential security challenges. In this work, we close this substantial gap and contribute a qualitative study focusing on developers' mental m
Externí odkaz:
http://arxiv.org/abs/2105.03726