Nowadays, organizations collect vast quantities of sensitive information in `Enterprise Resource Planning' (ERP) systems, such as accounting relevant transactions, customer master data, or strategic sales price information. The leakage of such information poses a severe threat for companies as the number of incidents and the reputational damage to those experiencing them continue to increase. At the same time, discoveries in deep learning research revealed that machine learning models could be maliciously misused to create new attack vectors. Understanding the nature of such attacks becomes increasingly important for the (internal) audit and fraud examination practice. The creation of such an awareness holds in particular for the fraudulent data leakage using deep learning-based steganographic techniques that might remain undetected by state-of-the-art `Computer Assisted Audit Techniques' (CAATs). In this work, we introduce a real-world `threat model' designed to leak sensitive accounting data. In addition, we show that a deep steganographic process, constituted by three neural networks, can be trained to hide such data in unobtrusive `day-to-day' images. Finally, we provide qualitative and quantitative evaluations on two publicly available real-world payment datasets.

翻译：目前,各组织在“企业资源规划”(ERP)系统中收集了大量敏感信息,例如会计相关交易、客户主数据或战略销售价格信息。这种信息的泄漏对公司构成严重威胁,因为事件数量和经历这些事件的人的名誉损害继续增加。与此同时,深层学习研究发现,机器学习模式可能被恶意滥用以创造新的攻击矢量。了解这种攻击的性质对于(内部)审计和欺诈审查做法越来越重要。建立这种认识特别有助于利用深层学习基础的精密视觉技术对数据泄漏进行欺诈,而这些技术可能仍然无法被最先进的“计算机辅助审计技术”(CAATs)所察觉。在这项工作中,我们采用了一种真实世界的“威胁模型”,旨在泄露敏感的会计数据。此外,我们表明,由三个神经网络构成的深层次的扫描过程,可以用来将这类数据隐藏在非侵入性的`日常'图像中。最后,我们提供两种公开现实支付数据的定性和定量评价。