---
res:
  bibo_abstract:
  - Despite extensive research since the community learned about adversarial examples
    10 years ago, we still do not know how to train high-accuracy classifiers that
    are guaranteed to be robust to small perturbations of their inputs. Previous works
    often argued that this might be because no classifier exists that is robust and
    accurate at the same time. However, in computer vision this assumption does not
    match reality where humans are usually accurate and robust on most tasks of interest.
    We offer an alternative explanation and show that in certain settings robust generalization
    is only possible with unrealistically large amounts of data. Specifically, we
    find a setting where a robust classifier exists, it is easy to learn an accurate
    classifier, yet it requires an exponential amount of data to learn a robust classifier.
    Based on this theoretical result, we evaluate the influence of the amount of training
    data on datasets such as CIFAR10. Our findings indicate that the the amount of
    training data is the main factor determining the robust performance. Furthermore
    we show that that there are low magnitude directions in the data which are useful
    for non-robust generalization but are not available for robust classifiers. This
    implies that robust classification is a strictly harder tasks than normal classification,
    thereby providing an explanation why robust classification requires more data.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Bernd
      foaf_name: Prach, Bernd
      foaf_surname: Prach
      foaf_workInfoHomepage: http://www.librecat.org/personId=2D561D42-C427-11E9-89B4-9C1AE6697425
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Lampert, Christoph
      foaf_surname: Lampert
      foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8622-7887
  bibo_doi: 10.1109/CVPRW67362.2025.00071
  dct_date: 2025^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2160-7508
  - http://id.crossref.org/issn/2160-7516
  - http://id.crossref.org/issn/9798331599942
  dct_language: eng
  dct_publisher: IEEE@
  dct_title: Intriguing properties of robust classification@
...