  Privacy protection is an important research area, which is especially
critical in this big data era. To a large extent, the privacy of visual
classification data is mainly in the mapping between the image and its
corresponding label, since this relation provides a great amount of information
and can be used in other scenarios. In this paper, we propose the mapping
distortion based protection (MDP) and its augmentation-based extension (AugMDP)
to protect the data privacy by modifying the original dataset. In the modified
dataset generated by MDP, the image and its label are not consistent ($e.g.$, a
cat-like image is labeled as the dog), whereas the DNNs trained on it can still
achieve good performance on benign testing set. As such, this method can
protect privacy when the dataset is leaked. Extensive experiments are
conducted, which verify the effectiveness and feasibility of our method. The
code for reproducing main results is available at
\url{https://github.com/PerdonLiu/Visual-Privacy-Protection-via-Mapping-Distortion}.

Cryptography and Security;Computer Vision and Pattern Recognition;Image and Video Processing