The rapid expansion of generative AI has normalized large-scale synthetic media creation, enabling new forms of covert communication. Recent generative steganography methods, particularly those based on diffusion models, can embed high-capacity payloads without fine-tuning or auxiliary decoders, creating significant challenges for detection and remediation. Coverless diffusion-based techniques are difficult to counter because they generate image carriers directly from secret data, enabling attackers to deliver stegomalware for command-and-control, payload staging, and data exfiltration while bypassing detectors that rely on cover-stego discrepancies. This work introduces Adversarial Diffusion Sanitization (ADS), a training-free defense for security gateways that neutralizes hidden payloads rather than detecting them. ADS employs an off-the-shelf pretrained denoiser as a differentiable proxy for diffusion-based decoders and incorporates a color-aware, quaternion-coupled update rule to reduce artifacts under strict distortion limits. Under a practical threat model and in evaluation against the state-of-the-art diffusion steganography method Pulsar, ADS drives decoder success rates to near zero with minimal perceptual impact. Results demonstrate that ADS provides a favorable security-utility trade-off compared to standard content transformations, offering an effective mitigation strategy against diffusion-driven steganography.

翻译：生成式人工智能的快速发展使得大规模合成媒体创作趋于常态化，从而催生了新型隐蔽通信方式。近期的生成式隐写方法，特别是基于扩散模型的技术，能够在无需微调或辅助解码器的情况下嵌入高容量载荷，这为检测与修复带来了严峻挑战。无载体的扩散式隐写技术因其直接从秘密数据生成图像载体而难以被对抗，攻击者可借此传递用于命令控制、载荷暂存和数据渗漏的隐写恶意软件，同时规避依赖载体-隐写差异的检测器。本研究提出对抗扩散净化（ADS），一种面向安全网关的无需训练防御机制，其核心在于消除隐藏载荷而非检测它们。ADS采用现成的预训练去噪器作为基于扩散的解码器可微分代理，并结合色彩感知的四元数耦合更新规则，以在严格失真限制下减少伪影。在实际威胁模型下，针对最先进的扩散隐写方法Pulsar进行评估，ADS在感知影响最小化的前提下将解码成功率降至接近零。结果表明，与标准内容变换相比，ADS实现了更优的安全-效用平衡，为抵御扩散驱动的隐写提供了有效的缓解策略。