As the popularity of Android smart phones has increased in recent years, so too has the number of malicious applications. Due to the potential for data the mobile phone users face, the detection of malware on Android devices has become an increasingly important issue in cyber security. Traditional methods like signature-based routines are unable to protect users from the ever-increasing sophistication and rapid behavior changes in new types of Android malware. erefore, a great deal of e ort has been made recently to use machine learning models and methods to characterize and generalize the malicious behavior pa erns of mobile apps for malware detection.In this paper, we propose a novel and highly reliable classi er for Android Malware detection based on a Factorization Machine architecture and the extraction of Android app features from manifest les and source code. Our results indicate that the numerical feature representation of an app typically results in a long and highly sparse vector and that the interactions among di erent features are critical to revealing malicious behavior pa erns. A er performing an extensive performance evaluation, our proposed method achieved a test result of 100.00% precision score on the DREBIN dataset and 99.22% precision score with only 1.10% false positive rate on the AMD dataset. ese metrics match the performance of state-of-the-art machine-learning-based Android malware detection methods and several commercial antivirus engines with the bene t of training up to 50 times faster.

Online advertisements are important elements in e-commerce sites, social media platforms, and search engines. With the increasing popularity of mobile browsing, many online ads are displayed with visual information in the form of a cover image in addition to text descriptions to grab the attention of users. Various recent studies have focused on predicting the click rates of online advertisements aware of visual features or composing optimal advertisement elements to enhance visibility. In this paper, we propose Advertisement Style Editing and Attractiveness Enhancement (AdSEE), which explores whether semantic editing to ads images can affect or alter the popularity of online advertisements. We introduce StyleGANbased facial semantic editing and inversion to ads images and train a click rate predictor attributing GAN-based face latent representations in addition to traditional visual and textual features to click rates. Through a large collected dataset named QQ-AD, containing 20,527 online ads, we perform extensive offline tests to study how different semantic directions and their edit coefficients may impact click rates. We further design a Genetic Advertisement Editor to efficiently search for the optimal edit directions and intensity given an input ad cover image to enhance its projected click rates. Online A/B tests performed over a period of 5 days have verified the increased click-through rates of AdSEE-edited samples as compared to a control group of original ads, verifying the relation between image styles and ad popularity. We open source the code for AdSEE research at https://github.com/LiyaoJiang1998/adsee.