SISA: Securing Images by Selective Alteration

TL;DR

This paper proposes a selective encryption and blurring technique for images that reduces computational overhead by encrypting only regions of interest identified through machine learning, balancing security and efficiency.

Contribution

It introduces a novel approach combining machine learning-based region detection with selective encryption and blurring for improved image security and reduced processing time.

Findings

Selective encryption lowers computational costs compared to full encryption.

Region of interest detection achieves high accuracy with Mask-RCNN and YOLO.

The system maintains security while improving usability and decryption speed.

Abstract

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.