Swapped Face Detection Using Deep Learning and Subjective Assessment

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Shivani Chaudhry, Dr. Krishna Kumar Singh

Abstract

Face swapping is an exciting new technology that allows the identification of a source face to be transferred to a target face while maintaining the target face's attributes. As a result, this study uses deep learning and subjective assessment for face swapping detection. In this study, face-swapping generates image-realistic and time-coherent sequences. Human recognition can aid in the prevention of face swapping attacks. Human volunteers determine accuracy in reconciling faces. They may believe a photograph is a forgery. Rankings of GIF emotions: A pair of Hamming-LUCBs is employed when you click an image. Selecting a switched type has a 50% failure rate. The CNN-LSTM and RNN-LSTM classifiers are compared. The RNN-LSTM algorithm outperformed the CNN-LSTM algorithm. Rather than that, we learned by exchanging faces. Validation made use of the system's default hyper parameters and epochs. We train and publish separately on both strategies. As a result, we compared them. 25-50-85 LSTM (CNN & RNN). We quantify the model's output. The dimensions of the faces are 0.2865 and 0.0415. R/F equals 0.1106. Real and fictitious faces have values of 0.3701 and 0.4229. Between 0.3741 and 0.1175. In Ae-GAN generated fictitious images, human connectedness is imprecise. The true identities of Nirkin and AEGAN (around 0.09 difference). This study makes extensive use of transfer learning. The Best Face Swap Detection Photos in the World Each model has over 1000 images taken in real life (the largest known). The model was confused because we put it to the test on humans. It makes a comparison of two photos. An image can identify an imposter; the context would then make it very evident that our model does not exist. As a result, the model can perceive a human. 

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