  Is it possible to detect a feature in an image without ever looking at it?
Images are known to have sparser representation in Wavelets and other similar
transforms. Compressed Sensing is a technique which proposes simultaneous
acquisition and compression of any signal by taking very few random linear
measurements (M). The quality of reconstruction directly relates with M, which
should be above a certain threshold for a reliable recovery. Since these
measurements can non-adaptively reconstruct the signal to a faithful extent
using purely analytical methods like Basis Pursuit, Matching Pursuit, Iterative
thresholding, etc., we can be assured that these compressed samples contain
enough information about any relevant macro-level feature contained in the
(image) signal. Thus if we choose to deliberately acquire an even lower number
of measurements - in order to thwart the possibility of a comprehensible
reconstruction, but high enough to infer whether a relevant feature exists in
an image - we can achieve accurate image classification while preserving its
privacy. Through the print error detection problem, it is demonstrated that
such a novel system can be implemented in practise.
