Neuromorphic audio in image steganography using spiking neural networks for secure and energy efficient data hiding

Abstract Secure data obfuscation requires balancing perceptual transparency, computational efficiency, and architectural deployability. Conventional spatial-domain steganography achieves high capacity but lacks structural abstraction and hardware-oriented design. We introduce SteganoSNN, a neuromorphic steganographic framework that integrates spike-based temporal encoding with field programmable gate array (FPGA)-accelerated embedding for secure and energy-aware multimedia data hiding. Audio samples are transformed into spike-count regimes using a Leaky Integrate-and-Fire (LIF) spiking neuron model, followed by modulo-based symbol transformation and deterministic spike-index mapping. The spike-to-Spike Index (SI) abstraction decouples source symbols from embedded bit patterns, introducing a temporal encoding layer prior to least significant bit (LSB) embedding in red, green, blue, alpha (RGBA) images. The proposed system is implemented in Python using NEST and realised on a PYNQ-Z2 FPGA through a hybrid processing-system/programming-logic co-design. Post-implementation analysis reports total on-chip power consumption of approximately 1.41 W, with programmable logic contributing only a small fraction of overall energy usage. The proposed framework supports an embedding capacity of 8 bits per pixel (bpp) while maintaining high perceptual fidelity on the DIV2K 2017 dataset, achieving peak signal-to-noise ratio (PSNR) values between 40.4 dB and 41.35 dB and structural similarity index (SSIM) above 0.97. Real-time feasibility is demonstrated through detailed latency and throughput evaluation on full-High Definition (HD) images. Unlike purely LSB-based approaches, SteganoSNN introduces a neuromorphic temporal representation layer that enables hardware-efficient symbol abstraction without increasing embedding depth. The results establish spike-based encoding as a viable architectural paradigm for secure and resource-aware steganography in edge-Artificial Intelligence (AI) and embedded systems.