  Duplicate detection is the problem of identifying whether a given item has
previously appeared in a (possibly infinite) stream of data, when only a
limited amount of memory is available.
  Unfortunately the infinite stream setting is ill-posed, and error rates of
duplicate detection filters turn out to be heavily constrained: consequently
they appear to provide no advantage, asymptotically, over a biased coin toss
[8].
  In this paper we formalize the sliding window setting introduced by [13,16],
and show that a perfect (zero error) solution can be used up to a maximal
window size $w_\text{max}$. Above this threshold we show that some existing
duplicate detection filters (designed for the $\textit{non-windowed}$ setting)
perform better that those targeting the windowed problem. Finally, we introduce
a "queuing construction" that improves on the performance of some duplicate
detection filters in the windowed setting.
  We also analyse the security of our filters in an adversarial setting.
