Toward the Confidential Data Location in Spatial Domain Images via a Genetic-based Pooling in a Convolutional Neural Network

Steganalysis, detecting hidden information within digital images, is crucial for securing data transmission. While current information security research focuses on non-adaptive steganography, there is a gap in addressing the challenge of locating payloads embedded through adaptive steganographic alg...

Full description

Saved in:
Bibliographic Details
Main Authors: de La Croix, Ntivuguruzwa Jean, Rachman Putra, Muhammad Aidiel, Ahmad, Tohari
Format: Conference Proceeding
Language:English
Subjects:
Adaptive systems
Classification algorithms
Digital images
genetic-based pooling
Image texture
Information security
network infrastructure
Spatial databases
spatial domain
steganalysis
Steganography
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Steganalysis, detecting hidden information within digital images, is crucial for securing data transmission. While current information security research focuses on non-adaptive steganography, there is a gap in addressing the challenge of locating payloads embedded through adaptive steganographic algorithms. This article introduces a novel steganalysis approach for the spatial domain. It identifies modification maps between two stego images and utilizes them as inputs to a convolutional neural network for pixel classification. Experimental assessments against adaptive steganographic algorithms WOW and S-UNIWARD consistently demonstrate superior performance, affirming the efficacy of the proposed strategy over existing methods. This methodology not only upholds network policies but also addresses the intricacies of adaptive steganography, enhancing overall security in digital image transmission.
ISSN:2154-4360
DOI:10.1109/ICCAE59995.2024.10569936