While successful, neural networks have been shown to be vulnerable to adversarial example attacks. In $L_0$ adversarial attacks, also known as few-pixel attacks, the attacker picks $t$ pixels from the image and arbitrarily perturbs them. To understand the robustness level of a network to these attacks, it is required to check the robustness of the network to perturbations of every set of $t$ pixels. Since the number of sets is exponentially large, existing robustness verifiers, which can reason about a single set of pixels at a time, are impractical for $L_0$ robustness verification. We introduce Calzone, an $L_0$ robustness verifier for neural networks. To the best of our knowledge, Calzone is the first to provide a sound and complete analysis for $L_0$ adversarial attacks. Calzone builds on the following observation: if a classifier is robust to any perturbation of a set of $k$ pixels, for $k>t$, then it is robust to any perturbation of its subsets of size $t$. Thus, to reduce the verification time, Calzone predicts the largest $k$ that can be proven robust, via dynamic programming and sampling. It then relies on covering designs to compute a covering of the image with sets of size $k$. For each set in the covering, Calzone submits its corresponding box neighborhood to an existing $L_\infty$ robustness verifier. If a set's neighborhood is not robust, Calzone repeats this process and covers this set with sets of size $k'<k$. We evaluate Calzone on several datasets and networks, for $t\leq 5$. Typically, Calzone verifies $L_0$ robustness within few minutes. On our most challenging instances (e.g., $t=5$), Calzone completes within few hours. We compare to a MILP baseline and show that it does not scale already for $t=3$.