Public image diffusion models are now powerful enough that an attacker without the resources to train a tabular-specific generator may repurpose one off the shelf. This study tests that possibility directly. An unmodified Stable Diffusion U-Net is applied to the UCI Adult Income dataset by reshaping each row into a small single-channel pseudo-image. The architecture's inductive bias toward spatial locality makes feature placement a design variable, and several layouts are tested. However, this is only the beginning of the story, as this paper also draws two philosophical distinctions. One separates statistical from perceptual realism: whether synthetic content holds up to a machine's correlation audits or a human's sensory inspection. The other introduces synthetic evidence as a category alongside synthetic media: AI-generated material whose consumer is a machine in a closed evidentiary pipeline rather than a person in an open information system. An attacker succeeds with synthetic evidence by thinking like the machine that will receive it. And the more the attacker succeeds, the more they can induce ground truth drift: the silent reclassification of AI-generated outputs as authentic when reused in pipelines that do not interrogate their provenance.

翻译：公开的图像扩散模型现已足够强大，缺乏资源训练表格专属生成器的攻击者可以直接使用现成模型进行攻击。本研究直接验证了这一可能性。通过将UCI成人收入数据集的每一行重塑为小型单通道伪图像，我们将未修改的Stable Diffusion U-Net应用于该数据集。该架构对空间局部性的归纳偏置使特征布局成为设计变量，本研究测试了多种布局方案。然而，这仅是研究的开端，本文还提出了两个哲学层面的区分。其一区分了统计真实性与感知真实性：合成内容是通过机器的相关性审计还是人类感官检验。其二将合成证据作为与合成媒体并列的类别提出：其消费者是封闭证据链中的机器，而非开放信息系统中的人类。攻击者通过模拟接收机器的思维模式成功实现合成证据攻击。攻击者越成功，就越能引发真实漂移：在未检验来源的流程中，AI生成输出被静默重新归类为真实数据。