现有的丢包主动测量方法是通过探测流的丢包信息去推测网络的丢包特性,进而推测特定应用流的丢包,测量结果不能准确获知某一给定应用流的丢包情况.由于丢包通常属于短时间、小概率事件,要更加准确地测量丢包就意味着需延长测量时间,或...现有的丢包主动测量方法是通过探测流的丢包信息去推测网络的丢包特性,进而推测特定应用流的丢包,测量结果不能准确获知某一给定应用流的丢包情况.由于丢包通常属于短时间、小概率事件,要更加准确地测量丢包就意味着需延长测量时间,或者提高探测流的发送速率以及时发现丢包,这将不可避免地增加网络的额外负载.分析了不同类型帧损伤的影响,并以MPEG-4,H264视频为研究对象,通过对其码流结构特点及RTP封装策略的分析,提出一种将测量信息嵌入到视频用户数据域(User_Data)的丢包测量方法 PLBU(packet loss measurement based on User_Data).该方法利用视频码流信息完成对丢包的探测,不影响视频的正常播放,不需要注入新的探测流,极大地降低了因测量而引入的额外负载.NIST Net及Planetlab等实验结果表明,该算法不仅丢包测量准确性高,且可提供丢包所属视频帧类型等信息,如视频中I,P,B帧的数据包丢失的情况.借助该测量方法,服务提供商可评测网络视频流丢包,并分析视频体验质量(QoE)变化情况,且不受视频流在网络传输中的优先级影响.展开更多
Deepfake technology can be used to replace people’s faces in videos or pictures to show them saying or doing things they never said or did. Deepfake media are often used to extort, defame, and manipulate public opini...Deepfake technology can be used to replace people’s faces in videos or pictures to show them saying or doing things they never said or did. Deepfake media are often used to extort, defame, and manipulate public opinion. However, despite deepfake technology’s risks, current deepfake detection methods lack generalization and are inconsistent when applied to unknown videos, i.e., videos on which they have not been trained. The purpose of this study is to develop a generalizable deepfake detection model by training convoluted neural networks (CNNs) to classify human facial features in videos. The study formulated the research questions: “How effectively does the developed model provide reliable generalizations?” A CNN model was trained to distinguish between real and fake videos using the facial features of human subjects in videos. The model was trained, validated, and tested using the FaceForensiq++ dataset, which contains more than 500,000 frames and subsets of the DFDC dataset, totaling more than 22,000 videos. The study demonstrated high generalizability, as the accuracy of the unknown dataset was only marginally (about 1%) lower than that of the known dataset. The findings of this study indicate that detection systems can be more generalizable, lighter, and faster by focusing on just a small region (the human face) of an entire video.展开更多
文摘现有的丢包主动测量方法是通过探测流的丢包信息去推测网络的丢包特性,进而推测特定应用流的丢包,测量结果不能准确获知某一给定应用流的丢包情况.由于丢包通常属于短时间、小概率事件,要更加准确地测量丢包就意味着需延长测量时间,或者提高探测流的发送速率以及时发现丢包,这将不可避免地增加网络的额外负载.分析了不同类型帧损伤的影响,并以MPEG-4,H264视频为研究对象,通过对其码流结构特点及RTP封装策略的分析,提出一种将测量信息嵌入到视频用户数据域(User_Data)的丢包测量方法 PLBU(packet loss measurement based on User_Data).该方法利用视频码流信息完成对丢包的探测,不影响视频的正常播放,不需要注入新的探测流,极大地降低了因测量而引入的额外负载.NIST Net及Planetlab等实验结果表明,该算法不仅丢包测量准确性高,且可提供丢包所属视频帧类型等信息,如视频中I,P,B帧的数据包丢失的情况.借助该测量方法,服务提供商可评测网络视频流丢包,并分析视频体验质量(QoE)变化情况,且不受视频流在网络传输中的优先级影响.
文摘Deepfake technology can be used to replace people’s faces in videos or pictures to show them saying or doing things they never said or did. Deepfake media are often used to extort, defame, and manipulate public opinion. However, despite deepfake technology’s risks, current deepfake detection methods lack generalization and are inconsistent when applied to unknown videos, i.e., videos on which they have not been trained. The purpose of this study is to develop a generalizable deepfake detection model by training convoluted neural networks (CNNs) to classify human facial features in videos. The study formulated the research questions: “How effectively does the developed model provide reliable generalizations?” A CNN model was trained to distinguish between real and fake videos using the facial features of human subjects in videos. The model was trained, validated, and tested using the FaceForensiq++ dataset, which contains more than 500,000 frames and subsets of the DFDC dataset, totaling more than 22,000 videos. The study demonstrated high generalizability, as the accuracy of the unknown dataset was only marginally (about 1%) lower than that of the known dataset. The findings of this study indicate that detection systems can be more generalizable, lighter, and faster by focusing on just a small region (the human face) of an entire video.
