In this paper, we investigated ChatGPT’s code transformation capability and the effectiveness of the code authorship attribution technique specially designed for ChatGPT code. Through our experiments, we made several key observations. Firstly, ChatGPT demonstrated the capability to transform code in ways that can mislead existing authorship attribution techniques by generating various styles, while it has some constraints, such as the maximum of 12 styles, with certain styles being more commonly employed than others. We also found that the feature-based code authorship attribution proved to be effective when even applied to ChatGPT-transformed code, while the naive approach encountered challenges with accurate classification. In addition, an authorship model trained for binary classification is still effective for ChatGPT-transformed code by achieving up to 93% accuracy. These findings provide insights into the code transformation ability of ChatGPT and shed light on the effectiveness of code authorship attribution techniques for ChatGPT-transformed code.

The development of metaverses and virtual worlds on various platforms, including mobile devices, has led to the growth of applications in virtual reality (VR) and augmented reality (AR) in recent years. This application growth is paralleled by a growth of interest in analyzing and understanding AR/VR applications from security and performance standpoints. Despite this growing interest, benchmark datasets are lacking to facilitate this research pursuit. In this article, we collect a dataset that consists of 408 diverse AR/VR applications from the Google Play Store and acquire various data modalities associated with those applications standardized in the form of seven features: control flow graphs, strings, functions, permissions, API calls, hexdump, and metadata. We highlight various research endeavors (applications) that can benefit from our dataset for each data modality.

This paper comprehensively analyzes privacy policies in AR/VR applications, leveraging BERT, a state-of-the-art text classification model, to evaluate the clarity and thoroughness of these policies. By comparing the privacy policies of AR/VR applications with those of free and premium websites, this study provides a broad perspective on the current state of privacy practices within the AR/VR industry. Our findings indicate that AR/VR applications generally offer a higher percentage of positive segments than free content but lower than premium websites. The analysis of highlighted segments and words revealed that AR/VR applications strategically emphasize critical privacy practices and key terms. This enhances privacy policies' clarity and effectiveness.