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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 ....

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