Abstract: Internet censorship research has been centered around notorious censors (e.g., China, Russia, and Iran) since they have developed complex and intricate censorship infrastructures that affect huge populations of people. However, there are other state-sponsored censors that deploy filtering apparatus as extensively, but which come from countries with a small population. These censors are often overlooked by the censorship research community due to the difficult nature in studying them. Current methods that exist to study censorship in such countries include deploying physical probes, recruiting volunteers to run experiments, using VPNs, or taking advantage of public infrastructures. Such methods suffer from two pitfalls, the need for participation from the endpoints and the lack of longitudinal measurements. To bridge this gap, we propose a censorship detection approach that takes advantage of middleboxes that implement bidirectional censorship and are not fully TCP-compliant. These features enable us to craft packet sequences that trigger censorship from outside the censored country without the need for endpoint participation. We do so by extending Geneva, an open-source genetic algorithm that discovers packet sequences that evade censorship. We repurpose Geneva: instead of searching for packet sequences to evade censorship, we search for packet sequences that trigger censorship. This paper is a first step towards applying this technique more broadly. We discuss how we intend to build this measurement system and introduce some preliminary results from detecting censorship in two underexplored nations: Brunei and Tajikistan.