# Evolutionary Algorithms and Efficient Data Analytics for Image Processing

## TL;DR

This paper explores how evolutionary algorithms can enhance real-time image processing by addressing the curse of dimensionality in steganalysis, aiming for a universal, efficient detection of hidden messages in images.

## Contribution

It investigates the application of evolutionary algorithms to improve real-time steganalysis and addresses the curse of dimensionality in feature-rich image analysis.

## Key findings

- Evolutionary algorithms show promise in solving the curse of dimensionality.
- They enable faster, more efficient steganalysis in real-time.
- Potential for developing universal steganalysis methods.

## Abstract

Steganography algorithms facilitate communication between a source and a destination in a secret manner. This is done by embedding messages/text/data into images without impacting the appearance of the resultant images/videos. Steganalysis is the science of determining if an image has secret messages embedded/hidden in it. Because there are numerous steganography algorithms, and since each one of them requires a different type of steganalysis, the steganalysis process is extremely challenging. Thus, researchers aim to develop one universal steganalysis to detect all known and unknown steganography algorithms, ideally in real-time. Universal steganalysis extracts a large number of features to distinguish stego images from cover images. However, the increase in features leads to the problem of the curse of dimensionality (CoD), which is considered to be an NP-hard problem. This COD problem additionally makes real-time steganalysis hard. A large number of features generates large datasets for which machine learning cannot generate an optimal model. Generating a machine learning based model also takes a long time which makes real-time processing appear impossible in any optimization for time-intensive fields such as visual computing. Possible solutions for CoD are deep learning and evolutionary algorithms that overcome the machine learning limitations. In this study, we investigate previously developed evolutionary algorithms for boosting real-time image processing and argue that they provide the most promising solutions for the CoD problem.