Social Media Image Retrieval Using Distilled Convolutional Neural Network for Suspicious e-Crime and Terrorist Account Detection
Pradip Chitrakar, Chengcui Zhang, Gary Warner, Xinpeng L. Liao · 2016
Retrieval of images with object-of-interest from a vast pool of social media images has been a research interest in cyber crime research community for detecting criminal behaviors in social media. Due to inherent diversity and the low duplicate property of images on social media, it brings forth many challenges in image retrieval, especially in identifying distinct features for a given object-of-interest. Previous literature approached this problem with extended General Hough Transform, where Hough space is analyzed for each specific object-of-interest. Different objects of interest produce different types of patterns in Hough space and no unified framework can be easily established to incorporate all those patterns. In this paper, we propose a unified framework based on convolutional neural network (CNN) for classifying the social media images and retrieving the images based on the probability score from the softmax classifier. In our framework, a reduced size CNN model is trained by distilling the knowledge from a pretrained full size CNN model, which is suitable for applications with limited training data such as ours and results in a higher accuracy in image retrieval as well as better performance in execution speed in comparison with the full size CNN model. Experiments on three image datasets relating to suspicious e-crime and terrorist involvement - Guy Fawkes masks, credit card logos, and ISIS logos show that our framework outperforms extended General Hough Transform and the full size CNN model.