The availability of software which can produce convincing yet synthetic media
poses both threats and benefits to tertiary education globally. While other
forms of synthetic media exist, this study focuses on deepfakes, which are
advanced Generative AI (GenAI) fakes of real people. This conceptual paper
assesses the current literature on deepfakes across multiple disciplines by
conducting an initial scoping review of 182 peer-reviewed publications.
The review reveals three major trends: detection methods, malicious
applications, and potential benefits, although no specific studies on deepfakes
in the tertiary educational context were found. Following a discussion of these
trends, this study applies the findings to postulate the major risks and
potential mitigation strategies of deepfake technologies in higher education,
as well as potential beneficial uses to aid the teaching and learning of both
deepfakes and synthetic media. This culminates in the proposal of a research
agenda to build a comprehensive, cross-cultural approach to investigate
deepfakes in higher education.

本文通过对 182 篇同行评议的出版物进行初步范围审查，评估了有关深度伪造的文献。通过讨论各方面的趋势，将结果应用于高等教育领域，提出了深度伪造技术在高等教育中的主要风险和潜在的缓解策略，以及有益于教学和学习的用途，最终提出了一个研究议程以构建一个全面的、跨文化的深度伪造教育研究方法。

深伪和高等教育：综合媒体的研究议程和范围回顾

Deepfakes and Higher Education: A Research Agenda and Scoping Review of  Synthetic Media

The Android operating system has been the most popular for smartphones and
tablets since 2012. This popularity has led to a rapid raise of Android malware
in recent years. The sophistication of Android malware obfuscation and
detection avoidance methods have significantly improved, making many
traditional malware detection methods obsolete. In this paper, we propose
DL-Droid, a deep learning system to detect malicious Android applications
through dynamic analysis using stateful input generation. Experiments performed
with over 30,000 applications (benign and malware) on real devices are
presented. Furthermore, experiments were also conducted to compare the
detection performance and code coverage of the stateful input generation method
with the commonly used stateless approach using the deep learning system. Our
study reveals that DL-Droid can achieve up to 97.8% detection rate (with
dynamic features only) and 99.6% detection rate (with dynamic + static
features) respectively which outperforms traditional machine learning
techniques. Furthermore, the results highlight the significance of enhanced
input generation for dynamic analysis as DL-Droid with the state-based input
generation is shown to outperform the existing state-of-the-art approaches.

本研究提出 DL-Droid，一种通过状态输入生成实现动态分析的深度学习系统，可成功检测安卓手机应用中的恶意程序。