Steganography is the art of hiding information in plain sight. This form of covert communication can be used by bad actors to propagate malware, exfiltrate victim data, and communicate with other bad actors. Current image steganography defenses rely upon steganalysis, or the detection of hidden messages. These methods, however, are non-blind as they require information about known steganography techniques and are easily bypassed. Recent work has instead focused on a defense mechanism known as sanitization, which eliminates hidden information from images. In this work, we introduce a novel blind deep learning steganography sanitization method that utilizes a diffusion model framework to sanitize universal and dependent steganography (DM-SUDS), which both sanitizes and preserves image quality. We evaluate this approach against state-of-the-art deep learning sanitization frameworks and provide further detailed analysis through an ablation study. DM-SUDS outperforms previous sanitization methods and improves image preservation MSE by 71.32%, PSNR by 22.43% and SSIM by 17.30%. This is the first blind deep learning image sanitization framework to meet these image quality results.

翻译：隐写术是一种将信息隐藏于众目睽睽之下的艺术。这种隐蔽通信手段可能被恶意行为者用于传播恶意软件、窃取受害者数据，并与其它恶意行为者进行通信。当前的图像隐写防御手段依赖于隐写分析，即检测隐藏信息。然而，这些方法是非盲的，因为它们需要已知隐写技术的相关信息，并且容易被绕过。近期研究转而聚焦于一种称为“清除”的防御机制，旨在消除图像中的隐藏信息。在本工作中，我们提出了一种新颖的盲深度学习隐写清除方法，该方法利用扩散模型框架来清除通用和依赖型隐写术（DM-SUDS），既能实现清除功能又能保持图像质量。我们针对最先进的深度学习清除框架进行了评估，并通过消融研究提供了进一步详细分析。DM-SUDS 优于以往的清除方法，在图像保真度方面将均方误差（MSE）提升71.32%，峰值信噪比（PSNR）提升22.43%，结构相似性指数（SSIM）提升17.30%。这是首个达到如此图像质量结果的盲深度学习图像清除框架。