  Counterfeit apps impersonate existing popular apps in attempts to misguide
users to install them for various reasons such as collecting personal
information, spreading malware, or simply to increase their advertisement
revenue. Many counterfeits can be identified once installed, however even a
tech-savvy user may struggle to detect them before installation as app icons
and descriptions can be quite similar to the original app. To this end, this
paper proposes to use neural embeddings generated by state-of-the-art
convolutional neural networks (CNNs) to measure the similarity between images.
Our results show that for the problem of counterfeit detection a novel approach
of using style embeddings given by the Gram matrix of CNN filter responses
outperforms baseline methods such as content embeddings and SIFT features. We
show that further performance increases can be achieved by combining style
embeddings with content embeddings. We present an analysis of approximately 1.2
million apps from Google Play Store and identify a set of potential
counterfeits for top-1,000 apps. Under a conservative assumption, we were able
to find 139 apps that contain malware in a set of 6,880 apps that showed high
visual similarity to one of the top-1,000 apps in Google Play Store.
