UIHash: Detecting Similar Android UIs through Grid-Based Visual Appearance Representation

User interfaces (UIs) is the main channel for users to interact with mobile apps. As such, attackers often create similar-looking UIs to deceive users, causing various security problems, such as spoofing and phishing. Prior studies identify these similar UIs based on their layout trees or screenshot images. These techniques, however, are susceptible to being evaded. Guided by how users perceive UIs and the features they prioritize, we design a novel grid-based UI representation to capture UI visual appearance while maintaining robustness against evasion. We develop an approach, UIHash, to detect similar Android UIs by comparing their visual appearance. It divides the UI into a #-shaped grid and abstracts UI controls across screen regions, then calculates UI similarity through a neural network architecture that includes a convolutional neural network and a Siamese network. Our evaluation shows that UIHash achieves an F1-score of 0.984 in detection, outperforming existing tree-based methods and image-based methods. Moreover, we have discovered evasion techniques that circumvent existing detection approaches.