Robust Deepfake Detection System with Deep Learning Techniques

Abstract

This comprehensive study investigates the pervasive issue of deep fakes within the context of deep learning applications, focusing on their detection and production. Utilizing a diverse array of deep learning algorithms, including InceptionResnetV2, VGG19, CNN, Xception, InceptionV3, EfficientNetB1, DenseNet121, Hybrid Model, LSTM, ResNext-LSTM, and MRI-GAN, the research systematically evaluates their effectiveness in detecting deep fakes. Results reveal varying levels of accuracy, with Xception emerging as the most precise algorithm, achieving an accuracy of 99.32%. Notably, InceptionResnetV2 and DenseNet121 also demonstrate robust performance, with accuracies surpassing 99%. However, certain models like VGG19 and LSTM exhibit lower accuracy rates, underscoring the need for further refinement. These findings underscore the urgent necessity for robust detection mechanisms amidst the proliferation of malicious deep fakes, safeguarding against potential societal ramifications such as misinformation and privacy breaches.