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Counterfeit apps impersonate existing popular apps in attempts to misguideusers to install them for various reasons such as collecting personalinformation, spreading malware, or simply to increase their advertisementrevenue. Many counterfeits can be identified once installed, however even atech-savvy user may struggle to detect them before installation as app iconsand descriptions can be quite similar to the original app. To this end, thispaper proposes to use neural embeddings generated by state-of-the-artconvolutional neural networks (CNNs) to measure the similarity between images.Our results show that for the problem of counterfeit detection a novel approachof using style embeddings given by the Gram matrix of CNN filter responsesoutperforms baseline methods such as content embeddings and SIFT features. Weshow that further performance increases can be achieved by combining styleembeddings with content embeddings. We present an analysis of approximately 1.2million apps from Google Play Store and identify a set of potentialcounterfeits for top-1,000 apps. Under a conservative assumption, we were ableto find 139 apps that contain malware in a set of 6,880 apps that showed highvisual similarity to one of the top-1,000 apps in Google Play Store.

A Neural Embeddings Approach for Detecting Mobile Counterfeit Apps