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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.20521 |
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| _version_ | 1866911614772445184 |
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| author | Guo, Jiamu Jia, Hailang Shen, Guanxiong Zhang, Junqing Peng, Linning Chen, Liquan |
| author_facet | Guo, Jiamu Jia, Hailang Shen, Guanxiong Zhang, Junqing Peng, Linning Chen, Liquan |
| contents | 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. Specifically, we introduce a CSI division mechanism to decouple artificial CSI components from natural wireless channel responses. In essence, secrets are embedded within the quotient of two consecutive CSI measurements. Furthermore, we propose an encoder-decoder neural network framework that automatically learns optimal strategies for FIR filter generation and secret recovery, substantially enhancing steganographic capacity. We implemented a prototype using commercial off-the-shelf hardware, including a software-defined radio (SDR) transmitter and two receiver platforms: ANTSDR and ESP32. Experimental evaluations demonstrate that the system achieves robust performance under dynamic environmental conditions while significantly improving steganographic capacity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20521 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hiding Secrets in the CSI Quotient: A Robust Wi-Fi CSI Steganography System Guo, Jiamu Jia, Hailang Shen, Guanxiong Zhang, Junqing Peng, Linning Chen, Liquan Signal Processing 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. Specifically, we introduce a CSI division mechanism to decouple artificial CSI components from natural wireless channel responses. In essence, secrets are embedded within the quotient of two consecutive CSI measurements. Furthermore, we propose an encoder-decoder neural network framework that automatically learns optimal strategies for FIR filter generation and secret recovery, substantially enhancing steganographic capacity. We implemented a prototype using commercial off-the-shelf hardware, including a software-defined radio (SDR) transmitter and two receiver platforms: ANTSDR and ESP32. Experimental evaluations demonstrate that the system achieves robust performance under dynamic environmental conditions while significantly improving steganographic capacity. |
| title | Hiding Secrets in the CSI Quotient: A Robust Wi-Fi CSI Steganography System |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.20521 |