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In event-driven wireless Visual Sensor Networks (wVSNs), video nodes have access to additional data from scalar-sensors such as temperature or motion. The scalar-data may be used locally by the nodes instead of (or in conjunction with) vision technologies to control the potentially energy-costly transmission and storage of video frames and must thus be reliable. In this work we focus on the detection of occasional errors in such scalar-data sensors under both the scenario of harsh environmental conditions, and the scenario of hostile conditions involving an attacker. In the hostile case, the attack statistics may not be known to the cluster-head performing the error detection. We hence propose the use of a count detector in conjunction with Nash equilibrium analysis for the hostile case. We compare the detection performance of the count detector in hostile conditions to the performance of the optimal Neyman-Pearson (NP ) detector which may be used under harsh conditions (scenario where the error statistics may be estimated). Through analysis and simulations we conclude that in this severe regime of attack with missing statistics, the count detector performs reasonably well compared with the optimal NP detector with significance for reliable event-driven wVSN.