  In this paper we investigate the usage of adversarial perturbations for the
purpose of privacy from human perception and model (machine) based detection.
We employ adversarial perturbations for obfuscating certain variables in raw
data while preserving the rest. Current adversarial perturbation methods are
used for data poisoning with minimal perturbations of the raw data such that
the machine learning model's performance is adversely impacted while the human
vision cannot perceive the difference in the poisoned dataset due to minimal
nature of perturbations. We instead apply relatively maximal perturbations of
raw data to conditionally damage model's classification of one attribute while
preserving the model performance over another attribute. In addition, the
maximal nature of perturbation helps adversely impact human perception in
classifying hidden attribute apart from impacting model performance. We
validate our result qualitatively by showing the obfuscated dataset and
quantitatively by showing the inability of models trained on clean data to
predict the hidden attribute from the perturbed dataset while being able to
predict the rest of attributes.
