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Steganography is the art of hiding information in plain sight. This form ofcovert communication can be used by bad actors to propagate malware, exfiltratevictim data, and communicate with other bad actors. Current image steganographydefenses rely upon steganalysis, or the detection of hidden messages. Thesemethods, however, are non-blind as they require information about knownsteganography techniques and are easily bypassed. Recent work has insteadfocused on a defense mechanism known as sanitization, which eliminates hiddeninformation from images. In this work, we introduce a novel blind deep learningsteganography sanitization method that utilizes a diffusion model framework tosanitize universal and dependent steganography (DM-SUDS), which both sanitizesand preserves image quality. We evaluate this approach against state-of-the-artdeep learning sanitization frameworks and provide further detailed analysisthrough an ablation study. DM-SUDS outperforms previous sanitization methodsand improves image preservation MSE by 71.32%, PSNR by 22.43% and SSIM by17.30%. This is the first blind deep learning image sanitization framework tomeet these image quality results.

Monsters in the Dark: Sanitizing Hidden Threats with Diffusion Models