Next Article in Journal
Nonequilibrium Thermodynamics in Biochemical Systems and Its Application
Previous Article in Journal
Computer Vision Based Automatic Recognition of Pointer Instruments: Data Set Optimization and Reading

Eigenfaces-Based Steganography

Institute of Computer Science, Pedagogical University of Krakow, 30-084 Krakow, Poland
Cryptography and Cognitive Informatics Laboratory, AGH University of Science and Technology, 30-059 Krakow, Poland
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz
Entropy 2021, 23(3), 273;
Received: 30 January 2021 / Revised: 17 February 2021 / Accepted: 22 February 2021 / Published: 25 February 2021
(This article belongs to the Section Information Theory, Probability and Statistics)
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. View Full-Text
Keywords: steganography; eigenfaces; linear combination; principal components analysis; Log-Euclidean Distance steganography; eigenfaces; linear combination; principal components analysis; Log-Euclidean Distance
Show Figures

Figure 1

MDPI and ACS Style

Hachaj, T.; Koptyra, K.; Ogiela, M.R. Eigenfaces-Based Steganography. Entropy 2021, 23, 273.

AMA Style

Hachaj T, Koptyra K, Ogiela MR. Eigenfaces-Based Steganography. Entropy. 2021; 23(3):273.

Chicago/Turabian Style

Hachaj, Tomasz, Katarzyna Koptyra, and Marek R. Ogiela. 2021. "Eigenfaces-Based Steganography" Entropy 23, no. 3: 273.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop