  With the rapid increase in online photo sharing activities, image obfuscation
algorithms become particularly important for protecting the sensitive
information in the shared photos. However, existing image obfuscation methods
based on hand-crafted principles are challenged by the dramatic development of
deep learning techniques. To address this problem, we propose to maximize the
distribution discrepancy between the original image domain and the encrypted
image domain. Accordingly, we introduce a collaborative training scheme: a
discriminator $D$ is trained to discriminate the reconstructed image from the
encrypted image, and an encryption model $G_e$ is required to generate these
two kinds of images to maximize the recognition rate of $D$, leading to the
same training objective for both $D$ and $G_e$. We theoretically prove that
such a training scheme maximizes two distributions' discrepancy. Compared with
commonly-used image obfuscation methods, our model can produce satisfactory
defense against the attack of deep recognition models indicated by significant
accuracy decreases on FaceScrub, Casia-WebFace and LFW datasets.
