SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks

2025-11-03

AI Summary

Deep joint source-channel coding (JSCC) suffers from severe semantic fidelity degradation under adversarial wireless conditions—e.g., pilot spoofing and subcarrier interference—exposing critical security vulnerabilities. To address this, we propose SecDiff, a diffusion-based robust semantic decoding framework. Our key contributions are: (1) a pseudo-inverse-guided sampling scheme with adaptive guidance weighting, enhancing semantic reconstruction efficiency and accuracy; (2) a power-aware subcarrier masking and mask repair co-strategy to mitigate interference attacks; and (3) an EM-driven joint pilot recovery and channel estimation optimization algorithm. Extensive experiments over OFDM channels demonstrate that SecDiff achieves a significantly superior trade-off between reconstruction quality (PSNR/SSIM) and computational overhead, consistently outperforming state-of-the-art secure and generative JSCC approaches across all evaluated metrics.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Distributed Search

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Deep joint source-channel coding (JSCC) has emerged as a promising paradigm for semantic communication, delivering significant performance gains over conventional separate coding schemes. However, existing JSCC frameworks remain vulnerable to physical-layer adversarial threats, such as pilot spoofing and subcarrier jamming, compromising semantic fidelity. In this paper, we propose SecDiff, a plug-and-play, diffusion-aided decoding framework that significantly enhances the security and robustness of deep JSCC under adversarial wireless environments. Different from prior diffusion-guided JSCC methods that suffer from high inference latency, SecDiff employs pseudoinverse-guided sampling and adaptive guidance weighting, enabling flexible step-size control and efficient semantic reconstruction. To counter jamming attacks, we introduce a power-based subcarrier masking strategy and recast recovery as a masked inpainting problem, solved via diffusion guidance. For pilot spoofing, we formulate channel estimation as a blind inverse problem and develop an expectation-minimization (EM)-driven reconstruction algorithm, guided jointly by reconstruction loss and a channel operator. Notably, our method alternates between pilot recovery and channel estimation, enabling joint refinement of both variables throughout the diffusion process. Extensive experiments over orthogonal frequency-division multiplexing (OFDM) channels under adversarial conditions show that SecDiff outperforms existing secure and generative JSCC baselines by achieving a favorable trade-off between reconstruction quality and computational cost. This balance makes SecDiff a promising step toward practical, low-latency, and attack-resilient semantic communications.

Problem

• SecDiff enhances security of deep JSCC against adversarial wireless attacks
• It addresses jamming via masked inpainting and pilot spoofing via blind estimation
• The method balances reconstruction quality with computational efficiency for semantic communication

Innovation

• Pseudoinverse-guided sampling with adaptive weighting
• Power-based subcarrier masking for jamming resistance
• EM-driven blind channel estimation against spoofing