What You See Is What You Get? It Is Not the Case! Detecting Misleading Icons for Mobile Applications
With the prevalence of smartphones, people nowadays can access a wide variety of services through diverse apps. A good Graphical User Interface (GUI) can make an app more appealing and competitive in app markets. Icon widgets, as an essential part of an app's GUI, leverage icons to visually convey their functionalities to facilitate user interactions. Whereas, designing intuitive icon widgets can be a non-trivial job. Developers should follow a series of guidelines and make appropriate choices from a plethora of possibilities. Inappropriately designed or misused icons may cause user confusion, lead to wrong operations, and even result in security risks (e.g., revenue loss and privacy leakage). To investigate the problem, we manually checked 9,075 icons of 1,111 top-ranked commercial apps from Google Play and found 640 misleading icons in 312 (~28%) of these apps. This shows that misleading icons are prevalent among real-world apps, even the top ones.
Manually identifying misleading icons to improve app quality is time-consuming and laborious. In this work, we propose the first framework, IconSeer, to automatically detect misleading icons for mobile apps. Our basic idea is to find the discrepancies between \textit{the commonly perceived intentions of an icon} and \textit{the actual functionality of the corresponding icon widget}. IconSeer takes an Android app as input and reports potential misleading icons. It is powered by a comprehensive icon-intention mapping constructed by analyzing 268,353 icons collected from 15,571 popular Android apps in Google Play. The mapping includes 179 icon classes and 852 intention classes. Given an icon widget under analysis, IconSeer first employs a pre-trained open-set deep learning model to infer the possible icon class and the potential intentions. IconSeer then extracts developer-specified text properties of the icon widget, which indicate the widget's actual functionality. Finally, IconSeer determines whether an icon is misleading by comparing the semantic similarity between the inferred intentions and the extracted text properties of the widget. We have evaluated IconSeer on the 1,111 Android apps with manually established ground truth. IconSeer successfully identified 1,172 inconsistencies (with an accuracy of 0.86), among which we further found 482 real misleading icons.
Wed 19 JulDisplayed time zone: Pacific Time (US & Canada) change
13:30 - 15:00 | ISSTA 7: Testing and Analysis of Machine-Learning SystemsTechnical Papers at Smith Classroom (Gates G10) Chair(s): Vincenzo Riccio University of Udine | ||
13:30 15mTalk | FairRec: Fairness Testing for Deep Recommender Systems Technical Papers Huizhong Guo Zhejiang University, Jinfeng Li Alibaba Group, Jingyi Wang Zhejiang University, Xiangyu Liu Alibaba Group, Dongxia Wang Zhejiang University, Zehong Hu Alibaba Group, Rong Zhang Alibaba Group, Hui Xue Alibaba Group DOI | ||
13:45 15mTalk | DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization Technical Papers Simin Chen University of Texas at Dallas, Shiyi Wei University of Texas at Dallas, Cong Liu University of California at Riverside, Wei Yang University of Texas at Dallas DOI | ||
14:00 15mTalk | Validating Multimedia Content Moderation Software via Semantic Fusion Technical Papers Wenxuan Wang Chinese University of Hong Kong, Jingyuan Huang Chinese University of Hong Kong, Chang Chen Chinese University of Hong Kong, Jiazhen Gu Chinese University of Hong Kong, Jianping Zhang Chinese University of Hong Kong, Weibin Wu Sun Yat-sen University, Pinjia He Chinese University of Hong Kong, Michael Lyu Chinese University of Hong Kong DOI | ||
14:15 15mTalk | What You See Is What You Get? It Is Not the Case! Detecting Misleading Icons for Mobile Applications Technical Papers Linlin Li Southern University of Science and Technology, Ruifeng Wang Northeastern University, Xian Zhan Southern University of Science and Technology, Ying Wang Northeastern University, Cuiyun Gao Harbin Institute of Technology, Sinan Wang Southern University of Science and Technology, Yepang Liu Southern University of Science and Technology DOI | ||
14:30 15mTalk | How Effective Are Neural Networks for Fixing Security Vulnerabilities Technical Papers Yi Wu Purdue University, Nan Jiang Purdue University, Hung Viet Pham University of Waterloo, Thibaud Lutellier University of Alberta, Jordan Davis Purdue University, Lin Tan Purdue University, Petr Babkin J.P. Morgan AI Research, Sameena Shah J.P. Morgan AI Research DOI | ||
14:45 15mTalk | ModelObfuscator: Obfuscating Model Information to Protect Deployed ML-Based Systems Technical Papers Mingyi Zhou Monash University, Xiang Gao Beihang University, Jing Wu Monash University, John Grundy Monash University, Xiao Chen Monash University, Chunyang Chen Monash University, Li Li Beihang University DOI | ||