  With an increase in mobile and camera devices' popularity, digital content in
the form of images has increased drastically. As personal life is being
continuously documented in pictures, the risk of losing it to eavesdroppers is
a matter of grave concern. Secondary storage is the most preferred medium for
the storage of personal and other images. Our work is concerned with the
security of such images. While encryption is the best way to ensure image
security, full encryption and decryption is a computationally-intensive
process. Moreover, as cameras are getting better every day, image quality, and
thus, the pixel density has increased considerably. The increased pixel density
makes encryption and decryption more expensive. We, therefore, delve into
selective encryption and selective blurring based on the region of interest.
Instead of encrypting or blurring the entire photograph, we only encode
selected regions of the image. We present a comparative analysis of the partial
and full encryption of the photos. This kind of encoding will help us lower the
encryption overhead without compromising security. The applications utilizing
this technique will become more usable due to the reduction in the decryption
time. Additionally, blurred images being more readable than encrypted ones,
allowed us to define the level of security. We leverage the machine learning
algorithms like Mask-RCNN (Region-based convolutional neural network) and YOLO
(You Only Look Once) to select the region of interest. These algorithms have
set new benchmarks for object recognition. We develop an end to end system to
demonstrate our idea of selective encryption.
