Coverless Steganography Based on Low Similarity Feature Selection in DCT Domain

Abstract

Coverless image steganography typically extracts feature sequences from cover images to map information. Once the extracted features have high similarity, it is challenging to construct a complete mapping sequence set, which places a heavy burden on the underlying storage and computation. In order to improve database utilization while increasing the data-hiding capacity, we propose a coverless steganography model based on low-similarity feature selection in the DCT domain. A mapping algorithm is presented based on an 8000-dimensional feature termed CS-DCTR extracted from each image to convert into binary sequences. The high feature dimension leads to a high capacity, ranging from 8 to 25 bits per image. Furthermore, scrambling is employed for feature mapping before building an inverted index tree, considerably enhancing security against steganalysis. Experimental results show that CS-DCTR features exhibit high diversity, averaging 49.3% complete mapping sequences, which indicates lower similarity among CS-DCTR features. The technique also demonstrates resistance to normal operations and benign attacks. The information extraction accuracy rises to 96.7% on average under typical noise attacks. Moreover, our technique achieves excellent performance in terms of hiding capacity, image utilization, and transmission security.