Shounak Shastri

Shounak Shastri

Researcher | Data Scientist | Machine Learning enthusiast

Publications

Dual Image Reversible Data Hiding Using Rhombus Prediction

Published in 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), 2019

This paper proposed a method to convert a normal Prediction Error Expansion based RDH algorithm to a Dual Image RDH algorithm. It was awarded the Best Paper certificate in the ViteCon 2019 conference.

Recommended citation: Shastri, S. Thanikaiselvan, V. (2019). "Dual Image Reversible Data Hiding Using Rhombus Prediction." 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN). 1(2). https://ieeexplore.ieee.org/document/8899667

Dual image reversible data hiding using trinary assignment and centre folding strategy with low distortion

Published in Journal of Visual Communication and Image Representation, 2019

This paper proposes a novel Dual Image RDH scheme which reduces the embedding distortion considerably while preserving the Embedding Capacity. We did this by designing a new encoding that converts binary data to “ready-to-embed” trinary data.

Recommended citation: S Shastri, V Thanikaiselvan (2019). "Dual image reversible data hiding using trinary assignment and centre folding strategy with low distortion." Journal of Visual Communication and Image Representation. 61. 130-140 https://doi.org/10.1016/j.jvcir.2019.03.022

Information Hiding-Steganography

Published in Intelligent Techniques in Signal Processing for Multimedia Security, 65-91, 2016

This is publication gives a review of some of the most popular steganography techniques being used in industry and academia. It was published as a book chapter.

Recommended citation: Thanikaiselvan V., Shastri S., Ahmad S. (2017). "Information Hiding: Steganography." In: Dey N., Santhi V. (eds) Intelligent Techniques in Signal Processing for Multimedia Security. Studies in Computational Intelligence, vol 660. Springer, Cham. https://doi.org/10.1007/978-3-319-44790-2_4