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
针对视频连续帧间匹配不准确、错误率高、匹配速度慢的问题,提出了一种改进的基于SURF(Speeded Up Robust Feature)特征点的匹配方法。按照SURF算法进行特征点检测和描述;对视频连续帧利用改进的最近邻与次近邻的比的方法进行双向匹配,...针对视频连续帧间匹配不准确、错误率高、匹配速度慢的问题,提出了一种改进的基于SURF(Speeded Up Robust Feature)特征点的匹配方法。按照SURF算法进行特征点检测和描述;对视频连续帧利用改进的最近邻与次近邻的比的方法进行双向匹配,在匹配时仅在以相应位置为中心的邻域内寻找最近邻点和次近邻点,根据最近距离与次近距离的比值与预先设定阈值的比较结果确定是否接受这一匹配点对;用RANSAC(Random Sample Consensus)方法建立变换矩阵模型剔除错误匹配点,得到精确匹配的特征点对,完成匹配过程。在经典的视频数据集上进行实验,实验结果表明该方法不仅提高了视频连续帧间匹配的正确率,同时使匹配时间相对缩短了一半左右,显著提高了匹配效率,证明了算法的有效性。展开更多
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.
