Using object-of-interest matching to detect presence of e-Crime activities in low-duplicate social media images is an interesting yet challenging problem that involves many complications due to the dataset's inherent diversity. SURF-based (Speeded Up Robust Features) object matching, though claimed to be scale and rotation invariant, is not effective as expected in this domain. This paper approaches this problem by an extended paradigm of Generalized Hough Transform using shape matching applied to two types of object-of-interest, Guy Fawkes Mask and Credit Card. We propose an extended GHT that updates the best matching score and the sum up score simultaneously, combined with a face detector and circular magnitude ranker, for detecting Guy Fawkes; also proposed is an extended GHT capable of mining the directional property in Hough space, combined with optical character recognition and an edge density filter, for detecting credit cards. Experiments on two real world datasets indicate that our approach outperforms the baseline GHT and the SURF.