Content-adaptive LSB steganography with saliency fusion, ACO dispersion, and hybrid encryption with ablation study

TL;DR

This paper introduces a new image steganography method that hides data imperceptibly using saliency maps, ACO dispersion, and hybrid encryption to balance invisibility, capacity, and security.

Contribution

A novel content-adaptive LSB steganography framework combining saliency fusion, ACO dispersion, and hybrid encryption with ablation study.

Findings

The framework achieves PSNR values of 59.7–60.2 dB for 64 × 64 secret images and up to 64.5 dB for 32 × 32 secrets.

The ACO-guided scheme shows random-level detectability against modern CNN-based steganalyzers.

Abstract

Image steganography is a security technique that conceals secret information within digital images in such a way that makes the hidden content imperceptible to human vision and difficult to detect statistically. The main challenge in image steganography lies in achieving an optimal balance among imperceptibility, embedding capacity, and security. To address these limitations, this paper proposes a content-adaptive Least Significant Bit (LSB) steganography framework that integrates saliency-guided embedding, Ant Colony Optimization (ACO)-based dispersion, and hybrid encryption to improve both invisibility and confidentiality. The system embeds secret data in low-sensitivity regions identified by a robust saliency fusion map, minimizing visual distortion. A block-wise ACO mechanism distributes embedding indices spatially across the image to prevent clustering artifacts and enhance undetectability. The framework removes side-information dependency by regenerating embedding indices deterministically from RSA-derived seeds, ensuring reproducibility during extraction. A hybrid cryptographic module combining Advanced Encryption Standard-Galois/Counter Mode (AES-GCM) and Advanced Encryption Standard-Optimal Asymmetric Encryption Padding (RSA-OAEP), with optional Hamming (7,4) error correction, guarantees confidentiality and reliable key recovery. The proposed framework demonstrates near-lossless imperceptibility, achieving PSNR values of 59.7–60.2 dB for 64 × 64 secret images and up to 64.5 dB for 32 × 32 secrets, with SSIM consistently above 0.999 and MSE below 0.07. Under capacity variations with Bits Per Pixel (BPP), the proposed system exhibits a clear rate–distortion behavior as PSNR decreases from 61.23 to 55.17 dB, while SSIM remains above 0.9978. All index-selection modes (ACO, random, perm, and saliency_topk) differ by less than 0.5 dB, confirming the stability of the LSB-robust content map, with saliency_topk achieving the highest PSNR \documentclass[12pt]{minimal}
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\begin{document}$$\approx$$\end{document} 60.16 dB. From a security perspective, the proposed ACO-guided scheme exhibits random-level detectability against modern CNN-based steganalyzers. Overall, the proposed method offers high imperceptibility, stable and lossless extraction under no-attack conditions, accurate payload rate control, and strong resistance to CNN-based steganalysis.

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