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Distortion Function for Emoji Image Steganography

Lina Shi1, Zichi Wang1, Zhenxing Qian1,*, Nannan Huang1, Pauline Puteaux2, Xinpeng Zhang1

Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China.
Laboratoire d'Informatique, de Robotique et de Microlectronique de Montpellier, Centre National de la Recherche Scientifique, University of Montpellier, Montpellier, 34095, France.

* Corresponding Author: Zhenxing Qian. Email: email.

Computers, Materials & Continua 2019, 59(3), 943-953.


Nowadays, emoji image is widely used in social networks. To achieve covert communication in emoji images, this paper proposes a distortion function for emoji images steganography. The profile of image content, the intra- and inter-frame correlation are taken into account in the proposed distortion function to fit the unique properties of emoji image. The three parts are combined together to measure the risks of detection due to the modification on the cover data. With the popular syndrome trellis coding (STC), the distortion of stego emoji image is minimized using the proposed distortion function. As a result, less detectable artifacts could be found in the stego images. Experimental results show that the proposed distortion function performs much higher undetectability than current state-of-the-art distortion function HILL which is designed for natural image.


Cite This Article

APA Style
Shi, L., Wang, Z., Qian, Z., Huang, N., Puteaux, P. et al. (2019). Distortion function for emoji image steganography. Computers, Materials & Continua, 59(3), 943-953.
Vancouver Style
Shi L, Wang Z, Qian Z, Huang N, Puteaux P, Zhang X. Distortion function for emoji image steganography. Comput Mater Contin. 2019;59(3):943-953
IEEE Style
L. Shi, Z. Wang, Z. Qian, N. Huang, P. Puteaux, and X. Zhang "Distortion Function for Emoji Image Steganography," Comput. Mater. Contin., vol. 59, no. 3, pp. 943-953. 2019.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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