Hiding Secrets in the CSI Quotient: A Robust Wi-Fi CSI Steganography System
Jiamu Guo, Hailang Jia, Guanxiong Shen, Junqing Zhang, Linning Peng, Liquan Chen

TL;DR
This paper introduces a robust Wi-Fi CSI steganography system that embeds secrets in the quotient of consecutive CSI measurements, employing neural networks to enhance capacity and resilience against environmental changes.
Contribution
It proposes a novel CSI division method combined with neural network-based encoding and decoding to improve capacity and robustness over existing Wi-Fi steganography techniques.
Findings
Achieves robust secret embedding under dynamic environmental conditions.
Significantly increases steganographic capacity compared to prior methods.
Successfully implemented with commercial hardware demonstrating practical viability.
Abstract
Physical layer (PHY) steganography conceals secrets by making subtle modifications to transmitted radio waveforms, which can be applied to establish covert communication systems. Given the widespread deployment of Wi-Fi infrastructures, hiding secrets within Wi-Fi transmissions exhibits significant covertness and has attracted increasing research attention. Recent advances in Wi-Fi steganography have focused on embedding secrets within channel state information (CSI) by applying artificial finite impulse response (FIR) filters to outgoing signals. These methods can emulate natural wireless propagation effects, thereby evading detection by eavesdroppers. However, existing CSI-based approaches suffer from two critical limitations: vulnerability to environmental variations and limited steganographic capacity. This work presents a Wi-Fi steganography system that mitigates these constraints.…
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