  We consider the group testing problem, in which one seeks to identify a
subset of defective items within a larger set of items based on a number of
noisy tests. While matching achievability and converse bounds are known in
several cases of interest for i.i.d.~measurement matrices, less is known
regarding converse bounds for arbitrary measurement matrices. We address this
by presenting two converse bounds for arbitrary matrices and general noise
models. First, we provide a strong converse bound ($\mathbb{P}[\mathrm{error}]
\to 1$) that matches existing achievability bounds in several cases of
interest. Second, we provide a weak converse bound ($\mathbb{P}[\mathrm{error}]
\not\to 0$) that matches existing achievability bounds in greater generality.
