Due to the power of editing tools,new types of fake faces are being created and synthesized,which has attracted great attention on social media.It is reasonable to acknowledge that one human cannot distinguish whether...Due to the power of editing tools,new types of fake faces are being created and synthesized,which has attracted great attention on social media.It is reasonable to acknowledge that one human cannot distinguish whether the face is manipulated from the real faces.Therefore,the detection of face manipulation becomes a critical issue in digital media forensics.This paper provides an overview of recent deep learning detection models for face manipulation.Some public dataset used for face manipulation detection is introduced.On this basis,the challenges for the research and the potential future directions are analyzed and discussed.展开更多
The rapid adoption of online social media platforms has transformed the way of communication and interaction.On these platforms,discussions in the form of trending topics provide a glimpse of events happening around t...The rapid adoption of online social media platforms has transformed the way of communication and interaction.On these platforms,discussions in the form of trending topics provide a glimpse of events happening around the world in real-time.Also,these trends are used for political campaigns,public awareness,and brand promotions.Consequently,these trends are sensitive to manipulation by malicious users who aim to mislead the mass audience.In this article,we identify and study the characteristics of users involved in the manipulation of Twitter trends in Pakistan.We propose“Manipify”-a framework for automatic detection and analysis of malicious users in Twitter trends.Our framework consists of three distinct modules:(1)user classifier,(2)hashtag classifier,and(3)trend analyzer.The user classifier module introduces a novel approach to automatically detect manipulators using tweet content and user behaviour features.Also,the module classifies human and bot users.Next,the hashtag classifier categorizes trending hashtags into six categories assisting in examining manipulators behaviour across different categories.Finally,the trend analyzer module examines users,hashtags,and tweets for hashtag reach,linguistic features,and user behaviour.Our user classifier module achieves 0.92 and 0.98 accuracy in classifying manipulators and bots,respectively.We further test Manipify on the dataset comprising 652 trending hashtags with 5.4 million tweets and 1.9 million users.The analysis of trends reveals that the trending panel is mostly dominated by political hashtags.In addition,our results show a higher contribution of human accounts in trend manipulation as compared to bots.展开更多
基金This work is supported by National Natural Science Foundation of China(62072251).
文摘Due to the power of editing tools,new types of fake faces are being created and synthesized,which has attracted great attention on social media.It is reasonable to acknowledge that one human cannot distinguish whether the face is manipulated from the real faces.Therefore,the detection of face manipulation becomes a critical issue in digital media forensics.This paper provides an overview of recent deep learning detection models for face manipulation.Some public dataset used for face manipulation detection is introduced.On this basis,the challenges for the research and the potential future directions are analyzed and discussed.
基金This work was supported by Higher Education Commission(HEC)Pakistan and Ministry of Planning Development and Reforms under National Center in Big Data and Cloud Computing.
文摘The rapid adoption of online social media platforms has transformed the way of communication and interaction.On these platforms,discussions in the form of trending topics provide a glimpse of events happening around the world in real-time.Also,these trends are used for political campaigns,public awareness,and brand promotions.Consequently,these trends are sensitive to manipulation by malicious users who aim to mislead the mass audience.In this article,we identify and study the characteristics of users involved in the manipulation of Twitter trends in Pakistan.We propose“Manipify”-a framework for automatic detection and analysis of malicious users in Twitter trends.Our framework consists of three distinct modules:(1)user classifier,(2)hashtag classifier,and(3)trend analyzer.The user classifier module introduces a novel approach to automatically detect manipulators using tweet content and user behaviour features.Also,the module classifies human and bot users.Next,the hashtag classifier categorizes trending hashtags into six categories assisting in examining manipulators behaviour across different categories.Finally,the trend analyzer module examines users,hashtags,and tweets for hashtag reach,linguistic features,and user behaviour.Our user classifier module achieves 0.92 and 0.98 accuracy in classifying manipulators and bots,respectively.We further test Manipify on the dataset comprising 652 trending hashtags with 5.4 million tweets and 1.9 million users.The analysis of trends reveals that the trending panel is mostly dominated by political hashtags.In addition,our results show a higher contribution of human accounts in trend manipulation as compared to bots.
