  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.
