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.
Sahoo et al. (Sun,) studied this question.