Blockchain Based Deepfake Video Detection Using Deep Learning

Deepfake identification has become an essential issue in the digital world as AI generated manipulated videos are being utilized more and more maliciously. In order to provide a trustworthy technique for detecting deep-fake video, proposed system employs an integrated deep learning method involving Long Short-Term Memory (LSTM), ResNeXt, and Convolutional Neural Networks (CNN). CNN and ResNeXt are used to analyze video frames and extract spatial data, whereas LSTM analyzes the temporal dynamics between frames. These obtained features are employed to determine the variations and irregularities frequently found in deepfake movies. The Python and PyTorch frameworks used in the model’s development and implementation provide high accuracy and efficiency. The results are verified as well and safely saved on blockchain, offering an infallible, transparent way to identify deepfakes. The fields of media integrity, digital forensics, and cybersecurity are greatly advanced by this program’s helpful tool for preventing online fraud and disinformation.