  Deep neural networks are increasingly deployed for scene analytics, including
to evaluate the attention and reaction of people exposed to out-of-home
advertisements. However, the features extracted by a deep neural network that
was trained to predict a specific, consensual attribute (e.g. emotion) may also
encode and thus reveal information about private, protected attributes (e.g.
age or gender). In this work, we focus on such leakage of private information
at inference time. We consider an adversary with access to the features
extracted by the layers of a deployed neural network and use these features to
predict private attributes. To prevent the success of such an attack, we modify
the training of the network using a confusion loss that encourages the
extraction of features that make it difficult for the adversary to accurately
predict private attributes. We validate this training approach on image-based
tasks using a publicly available dataset. Results show that, compared to the
original network, the proposed PrivateNet can reduce the leakage of private
information of a state-of-the-art emotion recognition classifier by 2.88% for
gender and by 13.06% for age group, with a minimal effect on task accuracy.
