Blind steganalysis performance is influenced by several factors including the features used for classification. This paper investigates the suitability of using lacunarity measure as a potential feature vector for blind steganalysis. Differential Box Counting (DBC) based lacunarity measure has been employed using the traditional sequential grid (SG) and a new radial strip (RS) approach. The performance of the multi-class SVM based classifier was unfortunately not what was expected. However, the findings show that both the SG and RS lacunarity produce enough discriminating features that warrant further research.