Distribution Discrepancy Maximization for Image Privacy Preserving
This addresses privacy concerns for users sharing images online, offering a novel defense against deep learning attacks, though it is incremental in improving upon existing obfuscation methods.
The paper tackles the problem of protecting sensitive information in shared photos by proposing a method to maximize distribution discrepancy between original and encrypted image domains, resulting in significant accuracy decreases on FaceScrub, Casia-WebFace, and LFW datasets.
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.