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Texture based steganalysis: results for color images Patricia Lafferty, Farid Ahmed
 

Summary: Texture based steganalysis: results for color images
Patricia Lafferty, Farid Ahmed
The Catholic University of America, 620 Michigan Avenue, NE, Washington, DC 20064
ABSTRACT
Steganographic and watermarking information inserted into a color image file, regardless of embedding algorithm,
causes disturbances in the relationships between neighboring pixels. A method for steganalysis utilizing the local
binary pattern (LBP) texture operator to examine the pixel texture patterns within neighborhoods across the color planes
is presented. Providing the outputs of this simple algorithm to an artificial neural net capable of supervised learning
results in the creation of a surprisingly reliable predictor of steganographic content, even with relatively small amounts
of embedded data. Other tools for identifying images with steganographic content have been developed by forming a
neural network input vector comprised of image statistics that respond to particular side effects of specific embedding
algorithms. The neural net in our experiment is trained with general texture related statistics from clean images and
images modified using only one embedding algorithm, and is able to correctly discriminate clean images from images
altered by data embedded by one of various different watermarking and steganographic algorithms. Algorithms tested
include various steganographic and watermarking programs and include spatial and transform domain image hiding
techniques. The interesting result is that clean color images can be reliably distinguished from steganographically
altered images based on texture alone, regardless of the embedding algorithm.
Keywords: Watermarking, Steganography, Steganalysis, LBP, Local Binary Pattern, Texture Analysis, Neural
Network
1. INTRODUCTION

  

Source: Ahmed, Farid - Department of Electrical Engineering and Computer Science, Catholic University of America

 

Collections: Engineering