Monsters in the Dark: Sanitizing Hidden Threats with Diffusion Models

• URL: http://arxiv.org/abs/2310.06951v1
• Date: Tue, 10 Oct 2023 19:15:11 GMT
• Title: Monsters in the Dark: Sanitizing Hidden Threats with Diffusion Models
• Authors: Preston K. Robinette, Daniel Moyer, Taylor T. Johnson,
• Abstract summary: Steganography is the art of hiding information in plain sight. Current image steganography defenses rely upon steganalysis, or the detection of hidden messages. Recent work has focused on a defense mechanism known as sanitization, which eliminates hidden information from images.
• Score: 4.443677138272269
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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