Christopher Schwartz

Adam Arthur, Christopher Schwartz

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.

Rebekah Overdorf, Christopher Schwartz

It is often said that war creates a fog in which it becomes difficult to discern friend from foe on the battlefield. In the ongoing war on fake accounts, conscious development of taxonomies of the phenomenon has yet to occur, resulting in much confusion on the digital battlefield about what exactly a fake account is. This paper intends to address this problem, not by proposing a taxonomy of fake accounts, but by proposing a systematic way to think taxonomically about the phenomenon. Specifically, we examine fake accounts through both a combined philosophical and computer science-based perspective. Through these lenses, we deconstruct narrow binary thinking about fake accounts, both in the form of general false dichotomies and specifically in relation to the Facebook's conceptual framework "Coordinated Inauthentic Behavior" (CIB). We then address the false dichotomies by constructing a more complex way of thinking taxonomically about fake accounts.