  Phishing, a continuously growing cyber threat, aims to obtain innocent users'
credentials by deceiving them via presenting fake web pages which mimic their
legitimate targets. To date, various attempts have been carried out in order to
detect phishing pages. In this study, we treat the problem of phishing web page
identification as an image classification task and propose a machine learning
augmented pure vision based approach which extracts and classifies compact
visual features from web page screenshots. For this purpose, we employed
several MPEG7 and MPEG7-like compact visual descriptors (SCD, CLD, CEDD, FCTH
and JCD) to reveal color and edge based discriminative visual cues. Throughout
the feature extraction process we have followed two different schemes working
on either whole screenshots in a "holistic" manner or equal sized "patches"
constructing a coarse-to-fine "pyramidal" representation. Moreover, for the
task of image classification, we have built SVM and Random Forest based machine
learning models. In order to assess the performance and generalization
capability of the proposed approach, we have collected a mid-sized corpus
covering 14 distinct brands and involving 2852 samples. According to the
conducted experiments, our approach reaches up to 90.5% F1 score via SCD. As a
result, compared to other studies, the suggested approach presents a
lightweight schema serving competitive accuracy and superior feature extraction
and inferring speed that enables it to be used as a browser plugin.
