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GANs (Generative Adversarial Networks) are widely employed in many domains of science and technology. They have produced high-resolution, photo-realistic faces that appear real to the human eye. However, recognizing these images is becoming increasingly difficult. This study introduces a new GAN ensemble model that is intended to improve GAN training issues (Mode Collapse and Convergence) and generate knowledge from diverse input samples. The proposed model is built using multiple CNN discriminators architecture based on the voting ensemble technique. It utilizes a modified diversity loss function designed with the goal of minimizing the distance between the generated and original distributions. It is proven as a robust technique for forgery detection, achieving 98.31% accuracy values. One of the major findings of the study is that the novel method outperforms existing GAN models based on a small dataset of "Face Mask Lite"(193 Unmasked images), using quantifiable parameters ....
DeepFake uses Generative+Adversarial Network for successfully switching the identities of two people. Large public databases and deep learning methods are now rapidly available because of the proliferation of easily accessible tools online. It has resulted in the emergence of very real appealing fake content that produced a bad impact and challenges for society to deal. Pre-trained generative adversarial networks (GANs) that can flawlessly substitute one person's face in a video or image for that other are proving supportive for implementing deepfake. This paper primarily presented a study of methods used to implement deepfake. Also, discuss the main deepfake's manipulation and detection techniques, and the implementation and detection of deepfake using Deep Convolution-based GAN models. A study of Comparative analyses of proposed GAN with other exiting GAN models using parameters Inception Score "IS"and Fréchet Inception Distance "FID"is also embedded. A ....
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