Adaptive Fuzzy Logic-Based Steganographic Encryption Framework: A Comprehensive Experimental Evaluation

TL;DR

This paper introduces an adaptive steganographic encryption framework that uses fuzzy logic to optimize pixel embedding depth based on local image features, enhancing security and reducing detectability.

Contribution

It combines fuzzy inference with cryptographic techniques to adaptively determine embedding depth, improving steganography's robustness and security compared to fixed methods.

Findings

Adaptive embedding reduces detectability in sensitive regions.

Fuzzy logic effectively guides pixel-wise embedding depth.

Cryptographic layer ensures payload confidentiality and integrity.

Abstract

Digital image steganography requires a careful trade-off among payload capacity, visual fidelity, and statistical undetectability. Fixed-depth least significant bit embedding remains attractive because of its simplicity and high capacity, but it modifies smooth and textured regions uniformly, thereby increasing distortion and detectability in statistically sensitive areas. This paper presents an adaptive steganographic framework that combines a Mamdanitype fuzzy inference system with modern authenticated encryption. The proposed method determines a pixel-wise embedding depth from 1 to 3 bits using local entropy, edge magnitude, and payload pressure as linguistic inputs. To preserve encoder-decoder synchronization, the same feature maps are computed from lower-bit-stripped images, making the adaptive control mechanism invariant to the least significant modifications introduced during embedding. A cryptographic layer based on Argon2id and AES-256-GCM protects payload confidentiality and integrity independently of steganographic concealment.