  Bias in classifiers is a severe issue of modern deep learning methods,
especially for their application in safety- and security-critical areas. Often,
the bias of a classifier is a direct consequence of a bias in the training
dataset, frequently caused by the co-occurrence of relevant features and
irrelevant ones. To mitigate this issue, we require learning algorithms that
prevent the propagation of bias from the dataset into the classifier. We
present a novel adversarial debiasing method, which addresses a feature that is
spuriously connected to the labels of training images but statistically
independent of the labels for test images. Thus, the automatic identification
of relevant features during training is perturbed by irrelevant features. This
is the case in a wide range of bias-related problems for many computer vision
tasks, such as automatic skin cancer detection or driver assistance. We argue
by a mathematical proof that our approach is superior to existing techniques
for the abovementioned bias. Our experiments show that our approach performs
better than state-of-the-art techniques on a well-known benchmark dataset with
real-world images of cats and dogs.
