An adaptive hybrid framework for secure image hiding using triple-transform keyed steganography
Year: 2026
Type: article
Abstract: The preservation of the perceptual quality of host images is the aim of Steganography techniques by concealing the information within digital media. Designing embedding strategies that maintain high visual fidelity while ensuring reliable payload recovery remains a key challenge in practical data hiding systems. This paper presents a transform-domain steganography framework that integrates discrete wavelet decomposition, block-based frequency analysis, and quantisation-driven embedding. The proposed approach embeds payload information in singular values extracted from the mid-band region of DCT blocks obtained from the LH subband of the wavelet domain. The embedding process is guided by a quantization index modulation scheme, while redundancy mechanisms and multi-carrier embedding are employed to improve extraction stability. The evaluation is performed on the BOSSBase image dataset, which provides a diverse collection of natural images for steganographic experiments. Experimental results achieve the high PSNR values ranging from 50 to 56 dB, structural similarity values 0.998533 while embedding indicates better imperceptibility of stego images. The robustness of the framework was further examined under JPEG compression. Under no attack conditions, the method achieved a BER of zero and maintained moderate extraction reliability in compression-induced perturbations in the coefficients. An ablation analysis was conducted to study the influence of the quantization step, repetition factor, and carrier selection on system performance. The observed trends confirm that the proposed embedding strategy provides a balanced compromise between visual imperceptibility and extraction robustness. The results demonstrate that the TTKS framework offers an effective solution for high-fidelity image steganography in transform-domain environments.
Source: Discover Applied Sciences
Authors: Juhi Singh, Arun Kumar Singh
Institutions: Manipal University Jaipur, Amity University Gurugram
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7225773282
FWCI:
Cites:
Cited by:
Related to: