Open Access

ARTICLE

A Dynamic YOLO-Based Sequence-Matching Model for Efficient Coverless Image Steganography

Jiajun Liu¹, Lina Tan^1,*, Zhili Zhou², Weijin Jiang¹, Yi Li¹, Peng Chen¹

1 School of Computer Science, Hunan University of Technology and Business, Changsha, 410205, China
2 Institute of Artificial Intelligence, Guangzhou University, Guangzhou, 510555, China

* Corresponding Author: Lina Tan. Email:

Computers, Materials & Continua 2024, 81(2), 3221-3240. https://doi.org/10.32604/cmc.2024.054542

Received 31 May 2024; Accepted 19 September 2024; Issue published 18 November 2024

Abstract

Many existing coverless steganography methods establish a mapping relationship between cover images and hidden data. One issue with these methods is that as the steganographic capacity increases, the number of images stored in the database grows exponentially. This makes it challenging to build and manage a large image database. To improve the image library utilization and anti-attack capability of the steganography system, we propose an efficient coverless scheme based on dynamically matched substrings. We utilize You Only Look Once (YOLO) for selecting optimal objects and create a mapping dictionary between these objects and scrambling factors. Using this dictionary, each image is effectively assigned to a specific scrambling factor, which is then used to scramble the receiver’s sequence key. To achieve sufficient steganography capability with a limited image library, all substrings of the scrambled sequences have the potential to hide data. After matching the secret information, the ideal number of stego images will be obtained from the database. According to experimental results, this technology outperforms most previous works in terms of data load, transmission security, and hiding capacity. It can recover an average of 79.85% of secret information under typical geometric attacks, and only approximately 200 random images are needed to achieve a capacity of 19 bits per image.

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Keywords

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