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pro vyhledávání: '"Geradts, Zeno"'
In high-stakes settings, Machine Learning models that can provide predictions that are interpretable for humans are crucial. This is even more true with the advent of complex deep learning based models with a huge number of tunable parameters. Recent
Externí odkaz:
http://arxiv.org/abs/2309.11155
This research evaluates a convolutional neural network (CNN) based approach to forensic video steganalysis. A video steganography dataset is created to train a CNN to conduct forensic steganalysis in the spatial domain. We use a noise residual convol
Externí odkaz:
http://arxiv.org/abs/2305.18070
Autor:
Zola, Francesco, Fernandez-Carrasco, Jose Alvaro, Bruse, Jan Lukas, Galar, Mikel, Geradts, Zeno
Biometric systems represent valid solutions in tasks like user authentication and verification, since they are able to analyze physical and behavioural features with high precision. However, especially when physical biometrics are used, as is the cas
Externí odkaz:
http://arxiv.org/abs/2208.00785
Publikováno v:
In Science & Justice September 2024 64(5):485-497
Publikováno v:
In Forensic Science International: Digital Investigation March 2024 48 Supplement
Publikováno v:
In Forensic Science International: Synergy 2024 8
Autor:
Geradts, Zeno, Riphagen, Quinten
Publikováno v:
In Forensic Science International: Synergy 2023 6
Calibration of score based likelihood ratio estimation in automated forensic facial image comparison
Publikováno v:
In Forensic Science International May 2022 334
Publikováno v:
In Forensic Science International: Digital Investigation March 2022 40
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