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Deep neural networks are increasingly deployed for scene analytics, includingto evaluate the attention and reaction of people exposed to out-of-homeadvertisements. However, the features extracted by a deep neural network thatwas trained to predict a specific, consensual attribute (e.g. emotion) may alsoencode and thus reveal information about private, protected attributes (e.g.age or gender). In this work, we focus on such leakage of private informationat inference time. We consider an adversary with access to the featuresextracted by the layers of a deployed neural network and use these features topredict private attributes. To prevent the success of such an attack, we modifythe training of the network using a confusion loss that encourages theextraction of features that make it difficult for the adversary to accuratelypredict private attributes. We validate this training approach on image-basedtasks using a publicly available dataset. Results show that, compared to theoriginal network, the proposed PrivateNet can reduce the leakage of privateinformation of a state-of-the-art emotion recognition classifier by 2.88% forgender and by 13.06% for age group, with a minimal effect on task accuracy.

Training privacy-preserving video analytics pipelines by suppressing features that reveal information about private attributes