@inproceedings{HartungHillgaertnerSchmitzetal.2014,
  author    = {Hartung, Frank and Hillg{\"a}rtner, Michael and Schmitz, G{\"u}nter and Schuba, Marko and Adolphs, Fabian and Hoffend, Jens and Theis, Jochen},
  title     = {IT-Sicherheit im Automobil},
  series = {AmE 2014 : Automotive meets Electronics, Beitr{\"a}ge der 5. GMM-Fachtagung vom 18. bis 19. Februar 2014 in Dortmund. (GMM-Fachbericht ; 78)},
  booktitle = {AmE 2014 : Automotive meets Electronics, Beitr{\"a}ge der 5. GMM-Fachtagung vom 18. bis 19. Februar 2014 in Dortmund. (GMM-Fachbericht ; 78)},
  publisher = {VDE-Verl.},
  address   = {Berlin},
  organization    = {VDE/VDI-Gesellschaft Mikroelektronik, Mikrosystem- und Feinwerktechnik (GMM)},
  isbn      = {978-3-8007-3580-8},
  pages     = {CD-ROM},
  year      = {2014},
  language  = {de}
}
@inproceedings{DinghoferHartung2020,
  author    = {Dinghofer, Kai and Hartung, Frank},
  title     = {Analysis of Criteria for the Selection of Machine Learning Frameworks},
  series = {2020 International Conference on Computing, Networking and Communications (ICNC)},
  booktitle = {2020 International Conference on Computing, Networking and Communications (ICNC)},
  publisher = {IEEE},
  address   = {New York, NY},
  doi       = {10.1109/ICNC47757.2020.9049650},
  pages     = {373 -- 377},
  year      = {2020},
  abstract  = {With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs.},
  language  = {en}
}
@inproceedings{GaldiHartungDugelay2019,
  author    = {Galdi, Chiara and Hartung, Frank and Dugelay, Jean-Luc},
  title     = {Socrates: A database of realistic data for source camera recognition on smartphones},
  series = {Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
  booktitle = {Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
  isbn      = {978-989-758-351-3},
  doi       = {10.5220/0007403706480655},
  pages     = {648 -- 655},
  year      = {2019},
  language  = {en}
}
@inproceedings{GaldiHartungDugelay2017,
  author    = {Galdi, Chiara and Hartung, Frank and Dugelay, Jean-Luc},
  title     = {Videos versus still images: Asymmetric sensor pattern noise comparison on mobile phones},
  series = {Electronic Imaging},
  booktitle = {Electronic Imaging},
  publisher = {Society for Imaging Science and Technology},
  address   = {Springfield, Virginia},
  issn      = {2470-1173},
  doi       = {10.2352/ISSN.2470-1173.2017.7.MWSF-331},
  pages     = {100 -- 103},
  year      = {2017},
  abstract  = {Nowadays, the most employed devices for recoding videos or capturing images are undoubtedly the smartphones. Our work investigates the application of source camera identification on mobile phones. We present a dataset entirely collected by mobile phones. The dataset contains both still images and videos collected by 67 different smartphones. Part of the images consists in photos of uniform backgrounds, especially collected for the computation of the RSPN. Identifying the source camera given a video is particularly challenging due to the strong video compression. The experiments reported in this paper, show the large variation in performance when testing an highly accurate technique on still images and videos.},
  language  = {en}
}