  A face image not only provides details about the identity of a subject but
also reveals several attributes such as gender, race, sexual orientation, and
age. Advancements in machine learning algorithms and popularity of sharing
images on the World Wide Web, including social media websites, have increased
the scope of data analytics and information profiling from photo collections.
This poses a serious privacy threat for individuals who do not want to be
profiled. This research presents a novel algorithm for anonymizing selective
attributes which an individual does not want to share without affecting the
visual quality of images. Using the proposed algorithm, a user can select
single or multiple attributes to be surpassed while preserving identity
information and visual content. The proposed adversarial perturbation based
algorithm embeds imperceptible noise in an image such that attribute prediction
algorithm for the selected attribute yields incorrect classification result,
thereby preserving the information according to user's choice. Experiments on
three popular databases i.e. MUCT, LFWcrop, and CelebA show that the proposed
algorithm not only anonymizes k-attributes, but also preserves image quality
and identity information.
