  Deep learning has been widely applied in many computer vision applications,
with remarkable success. However, running deep learning models on mobile
devices is generally challenging due to the limitation of computing resources.
A popular alternative is to use cloud services to run deep learning models to
process raw data. This, however, imposes privacy risks. Some prior arts
proposed sending the features extracted from raw data to the cloud.
Unfortunately, these extracted features can still be exploited by attackers to
recover raw images and to infer embedded private attributes. In this paper, we
propose an adversarial training framework, DeepObfuscator, which prevents the
usage of the features for reconstruction of the raw images and inference of
private attributes. This is done while retaining useful information for the
intended cloud service. DeepObfuscator includes a learnable obfuscator that is
designed to hide privacy-related sensitive information from the features by
performing our proposed adversarial training algorithm. The proposed algorithm
is designed by simulating the game between an attacker who makes efforts to
reconstruct raw image and infer private attributes from the extracted features
and a defender who aims to protect user privacy. By deploying the trained
obfuscator on the smartphone, features can be locally extracted and then sent
to the cloud. Our experiments on CelebA and LFW datasets show that the quality
of the reconstructed images from the obfuscated features of the raw image is
dramatically decreased from 0.9458 to 0.3175 in terms of multi-scale structural
similarity. The person in the reconstructed image, hence, becomes hardly to be
re-identified. The classification accuracy of the inferred private attributes
that can be achieved by the attacker is significantly reduced to a
random-guessing level.
