DBIS

In this thesis, we investigate the risk of a Singling Out on visual data.
This is closely tied to the potential risk of visual re-identification attacks.
We investigate this risk by providing a conceptual approach of detecting
bodies in visual data. After that anonymizing their faces using a Gaussian
Blur and finally collecting visual attributes, such as age, gender, and more.
Those are then analyzed for their risk of Singling Out. Furthermore, we
provide an analysis on how much the usage of anonymization techniques
impact the classification process of visual attributes. It was discovered
that a singling out was partly possible for some people in the dataset,
but not for all. In addition, for some visual attributes the anonymization-
process did not yield a significant deviation from the ground truth.