A high‐capacity and secure least significant bit embedding approach based on word and letter frequencies

Abstract

In this paper, we propose a novel least significant bit embedding approach that capitalizes on the skewed distribution of letter and word frequencies to achieve higher image capacity, quality, and security. We initially conduct a study that involves all of the character frequencies using a data set of 14.245 billion characters. Huffman coding for each character is generated on the basis of its probability of occurrence. Furthermore, the top 100 000 most frequent words are transformed into a smaller ciphertext that has a lower cost. Our work demonstrates that recognizing characters and words on the basis of their frequency patterns and prioritizing them accordingly has a greater prospect of reducing the overall cost of embedding. The proposed scheme significantly outperforms Lempel-Ziv-Welch compression with an average of 45% fewer embedded bits. Moreover, the image quality is improved by a mean peak signal-to-noise ratio value of 6.9%. The proposed method also establishes a security embedding by proposing a novel shuffling algorithm.