Deepfake Detection Using CNN and DCGANS to Drop-Out Fake Multimedia Content: A Hybrid Approach
Kartik Bansal, Shubhi Agarwal, Narayan Vyas
Abstract
The creation of Deep Fakes, which are altered videos, audio, and photographs capable of disseminating false information and fake news and modifying sensitive records, is the result of the rapid advancements in artificial intelligence and machine learning. DeepFakes may also be used for interactive learning and visual effects in entertainment and education. As a result, numerous deep learning models, such as Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN), are being used for detection. DeepFakes detection and removal have become essential challenges. Facebook AI's Deepfake Detection Challenge (DFDC) dataset is invaluable for developing and evaluating detection techniques. While it represents serious risks, creating trustworthy detection techniques might lessen their impact and enable investigation of their possible beneficial applications. To ensure the authenticity and dependability of multimedia information in the face of the ongoing DeepFake threat, this paper emphasizes the importance of transfer learning, deep learning, and optimization techniques in building effective detection models. By doing this, we can stop the spread of fake news and information, protect the public's trust, and promote the moral and beneficial application of DeepFake technology across various fields.