Search papers, labs, and topics across Lattice.
This paper introduces an adaptive image steganography framework that dynamically adjusts the LSB embedding depth (1-3 bits) based on local image characteristics (entropy, edge magnitude) and payload pressure using a Mamdani-type fuzzy inference system. The system maintains encoder-decoder synchronization by computing feature maps from lower-bit-stripped images, ensuring robustness against LSB modifications. By integrating Argon2id and AES-256-GCM, the framework provides cryptographic protection of the payload, enhancing both confidentiality and integrity.
Steganography gets smarter: this framework hides data more effectively by adapting the amount of information concealed in each pixel based on image complexity and payload size.
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.
