SteganoSNN: SNN-Based Audio-in-Image Steganography with Encryption

• URL: http://arxiv.org/abs/2511.06573v1
• Date: Sun, 09 Nov 2025 23:31:53 GMT
• Title: SteganoSNN: SNN-Based Audio-in-Image Steganography with Encryption
• Authors: Biswajit Kumar Sahoo, Pedro Machado, Isibor Kennedy Ihianle, Andreas Oikonomou, Srinivas Boppu,
• Abstract summary: This work introduces SteganoSNN, a neuromorphic steganographic framework that exploits spiking neural networks (SNNs) to achieve secure, low-power, and high-capacity multimedia data hiding.<n> Digitised audio samples are converted into spike trains using leaky integrate-and-fire neurons, encrypted via a modulo-based mapping scheme, and embedded into the least significant bits of RGBA image channels.
• Score: 1.3483884526104932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Secure data hiding remains a fundamental challenge in digital communication, requiring a careful balance between computational efficiency and perceptual transparency. The balance between security and performance is increasingly fragile with the emergence of generative AI systems capable of autonomously generating and optimising sophisticated cryptanalysis and steganalysis algorithms, thereby accelerating the exposure of vulnerabilities in conventional data-hiding schemes. This work introduces SteganoSNN, a neuromorphic steganographic framework that exploits spiking neural networks (SNNs) to achieve secure, low-power, and high-capacity multimedia data hiding. Digitised audio samples are converted into spike trains using leaky integrate-and-fire (LIF) neurons, encrypted via a modulo-based mapping scheme, and embedded into the least significant bits of RGBA image channels using a dithering mechanism to minimise perceptual distortion. Implemented in Python using NEST and realised on a PYNQ-Z2 FPGA, SteganoSNN attains real-time operation with an embedding capacity of 8 bits per pixel. Experimental evaluations on the DIV2K 2017 dataset demonstrate image fidelity between 40.4 dB and 41.35 dB in PSNR and SSIM values consistently above 0.97, surpassing SteganoGAN in computational efficiency and robustness. SteganoSNN establishes a foundation for neuromorphic steganography, enabling secure, energy-efficient communication for Edge-AI, IoT, and biomedical applications.

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