Sanitizing Hidden Information with Diffusion Models

TL;DR

This paper introduces DM-SUDS, a diffusion model-based method for effectively sanitizing hidden information in images, outperforming previous deep learning approaches and applicable across multiple domains.

Contribution

The paper presents a novel diffusion model framework for scalable and effective sanitization of hidden information in images, surpassing prior methods in both performance and applicability.

Findings

DM-SUDS outperforms baseline methods in image quality metrics.

The approach significantly reduces hidden information while preserving image utility.

The method demonstrates versatility through an audio case study.

Abstract

Information hiding is the process of embedding data within another form of data, often to conceal its existence or prevent unauthorized access. This process is commonly used in various forms of secure communications (steganography) that can be used by bad actors to propagate malware, exfiltrate victim data, and discreetly communicate. Recent work has utilized deep neural networks to remove this hidden information in a defense mechanism known as sanitization. Previous deep learning works, however, are unable to scale efficiently beyond the MNIST dataset. In this work, we present a novel sanitization method called DM-SUDS that utilizes a diffusion model framework to sanitize/remove hidden information from image-into-image universal and dependent steganography from CIFAR-10 and ImageNet datasets. We evaluate DM-SUDS against three different baselines using MSE, PSNR, SSIM, and NCC metrics and provide further detailed analysis through an ablation study. DM-SUDS outperforms all three baselines and significantly improves image preservation MSE by 50.44%, PSNR by 12.69%, SSIM by 11.49%, and NCC by 3.26% compared to previous deep learning approaches. Additionally, we introduce a novel evaluation specification that considers the successful removal of hidden information (safety) as well as the resulting quality of the sanitized image (utility). We further demonstrate the versatility of this method with an application in an audio case study, demonstrating its broad applicability to additional domains.