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

Computer Science > Cryptography and Security [Submitted on 18 Mar 2026] Adaptive Fuzzy Logic-Based Steganographic Encryption Framework: A Comprehensive Experimental Evaluation Aadi Joshi, Kavya Bhand 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. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.18105 [cs.CR]   (or arXiv:2603.18105v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.18105 Focus to learn more Submission history From: Kavya Bhand [view email] [v1] Wed, 18 Mar 2026 12:43:41 UTC (16 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)