Paper Title
Advanced Deep Learning Models For Reliable Detection of DeepFake Media Content

Abstract
This is the thorough analysis of the important topic of deep fakes with Deep Learning (DL), their location, and creation. The paper evaluates the performance of different DL CNN, Xception, Inception V3, InceptionResnetV2, VGG19, EfficientNetB1, DenseNet121, Hybrid Model, LSTM, ResNext-LSTM and MRI-GAN are models, represented numerically process of detecting deep fakes. The findings reveal that the other algorithms are less precise than the Xception method which is at 99.32 accurate. The inceptionResnetV2 and DenseNet121 are also rather useful, and their accuracy rates are 99 and 99, respectively. However, other models, such as the VGG19 and LSTM have not been as effective, which translates to the potential of them being refined. These findings demonstrate the significance of a proper means of detection that prevents the proliferation of malicious deep fakes that might cause issues such as fake. Keywords - Deep Learning, Fake Detection, Inception ResnetV2, VGG19, CNN, and Xception