---
res:
  bibo_abstract:
  - While convolutional neural networks (CNNs) have found wide adoption as state-of-the-art
    models for image-related tasks, their predictions are often highly sensitive to
    small input perturbations, which the human vision is robust against. This paper
    presents Perturber, a web-based application that allows users to instantaneously
    explore how CNN activations and predictions evolve when a 3D input scene is interactively
    perturbed. Perturber offers a large variety of scene modifications, such as camera
    controls, lighting and shading effects, background modifications, object morphing,
    as well as adversarial attacks, to facilitate the discovery of potential vulnerabilities.
    Fine-tuned model versions can be directly compared for qualitative evaluation
    of their robustness. Case studies with machine learning experts have shown that
    Perturber helps users to quickly generate hypotheses about model vulnerabilities
    and to qualitatively compare model behavior. Using quantitative analyses, we could
    replicate users’ insights with other CNN architectures and input images, yielding
    new insights about the vulnerability of adversarially trained models.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Stefan
      foaf_name: Sietzen, Stefan
      foaf_surname: Sietzen
  - foaf_Person:
      foaf_givenName: Mathias
      foaf_name: Lechner, Mathias
      foaf_surname: Lechner
      foaf_workInfoHomepage: http://www.librecat.org/personId=3DC22916-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Judy
      foaf_name: Borowski, Judy
      foaf_surname: Borowski
  - foaf_Person:
      foaf_givenName: Ramin
      foaf_name: Hasani, Ramin
      foaf_surname: Hasani
  - foaf_Person:
      foaf_givenName: Manuela
      foaf_name: Waldner, Manuela
      foaf_surname: Waldner
  bibo_doi: 10.1111/cgf.14418
  bibo_issue: '7'
  bibo_volume: 40
  dct_date: 2021^xs_gYear
  dct_identifier:
  - UT:000722952000024
  dct_isPartOf:
  - http://id.crossref.org/issn/0167-7055
  - http://id.crossref.org/issn/1467-8659
  dct_language: eng
  dct_publisher: Wiley@
  dct_title: Interactive analysis of CNN robustness@
...