  Recent works in cyber deception study how to deter malicious intrusion by
generating multiple fake versions of a critical document to impose costs on
adversaries who need to identify the correct information. However, existing
approaches are context-agnostic, resulting in sub-optimal and unvaried outputs.
We propose a novel context-aware model, Fake Document Infilling (FDI), by
converting the problem to a controllable mask-then-infill procedure. FDI masks
important concepts of varied lengths in the document, then infills a realistic
but fake alternative considering both the previous and future contexts. We
conduct comprehensive evaluations on technical documents and news stories.
Results show that FDI outperforms the baselines in generating highly believable
fakes with moderate modification to protect critical information and deceive
adversaries.
