Eigenfaces-Based Steganography

Abstract

In this paper we propose a novel transform domain steganography technique—hiding a message in components of linear combination of high order eigenfaces vectors. By high order we mean eigenvectors responsible for dimensions with low amount of overall image variance, which are usually related to high-frequency parameters of image (details). The study found that when the method was trained on large enough data sets, image quality was nearly unaffected by modification of some linear combination coefficients used as PCA-based features. The proposed method is only limited to facial images, but in the era of overwhelming influence of social media, hundreds of thousands of selfies uploaded every day to social networks do not arouse any suspicion as a potential steganography communication channel. From our best knowledge there is no description of any popular steganography method that utilizes eigenfaces image domain. Due to this fact we have performed extensive evaluation of our method using at least 200 000 facial images for training and robustness evaluation of proposed approach. The obtained results are very promising. What is more, our numerical comparison with other state-of-the-art algorithms proved that eigenfaces-based steganography is among most robust methods against compression attack. The proposed research can be reproduced because we use publicly accessible data set and our implementation can be downloaded.

WITHDRAWN: Hybrid Architecture for Secure Data Communication Within the Private Cloud

“…Data may be stored in the cloud and cloud computing used online, but the issue with data security still exists [11]. The two-way technique provides security information in picture format [12]. The security of image-based data stored in the cloud is increased by integrating the two techniques.…”

On the Search for Potentially Anomalous Traces of Cosmic Ray Particles in Images Acquired by Cmos Detectors for a Continuous Stream of Emerging Observational Data

“…To make this 2d to 1d conversion we store each row of the image one by another in a single row vector. In order to calculate PCA-based embedding (we will call it also “basic PCA”) of image we can adapt eigenfaces-based image representation similar to one used in [ 110 ]. In order to do so we need to create a matrix that contains all observations: where X has dimensions.…”

Steganography in IoT: Information Hiding with Joystick and Touch Sensors

“…These techniques use as embedding region singular vectors, singular values or combinations of them [ 26 , 27 , 28 , 29 ]. There are also steganographic methods that use principal component analysis to facial images, which is called eigenfaces [ 30 ]. These algorithms generally include encryption for additional security, the examples may be found among image [ 31 ] and video steganography [ 32 ].…”