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
An improved analytical design to investigate the static stiffness of a convoluted air spring is developed and presented in this article.An air spring provides improved ride comfort by achieving variable volume at vari...An improved analytical design to investigate the static stiffness of a convoluted air spring is developed and presented in this article.An air spring provides improved ride comfort by achieving variable volume at various strokes of the suspension.An analytical relation is derived to calculate the volume and the rate of change in the volume of the convoluted bellow with respect to various suspension heights.This expression is used in the equation to calculate the variable stiffness of the bellow.The obtained analytical characteristics are validated with a detailed experiment to test the static vertical stiffness of the air spring.The convoluted air bellow is tested in an Avery spring-testing apparatus for various loads.The bellow is modeled in the ABAQUS environment to perform finite element analysis(FEA)to understand and visualize the deflection of the bellow at various elevated internal pressures and external loads.The proposed air spring model is a fiber-reinforced rubber bellow enclosed between two metal plates.The Mooney-Rivlin material model was used to model the hyperelastic rubber material for FEA.From the results,it is observed that the experimental and analytical results match with a minor error of 7.54%.The derived relations and validations would provide design guidance at the developmental stage of air bellows.These expressions would also play a major role in designing an effective active air suspension system by accurately calculating the required stiffness at various loads.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.
文摘An improved analytical design to investigate the static stiffness of a convoluted air spring is developed and presented in this article.An air spring provides improved ride comfort by achieving variable volume at various strokes of the suspension.An analytical relation is derived to calculate the volume and the rate of change in the volume of the convoluted bellow with respect to various suspension heights.This expression is used in the equation to calculate the variable stiffness of the bellow.The obtained analytical characteristics are validated with a detailed experiment to test the static vertical stiffness of the air spring.The convoluted air bellow is tested in an Avery spring-testing apparatus for various loads.The bellow is modeled in the ABAQUS environment to perform finite element analysis(FEA)to understand and visualize the deflection of the bellow at various elevated internal pressures and external loads.The proposed air spring model is a fiber-reinforced rubber bellow enclosed between two metal plates.The Mooney-Rivlin material model was used to model the hyperelastic rubber material for FEA.From the results,it is observed that the experimental and analytical results match with a minor error of 7.54%.The derived relations and validations would provide design guidance at the developmental stage of air bellows.These expressions would also play a major role in designing an effective active air suspension system by accurately calculating the required stiffness at various loads.
