Deepfakes are a form of synthetic media that use deep learning techniques to create fake images, videos, or audio recordings. These deepfake media can be used to impersonate individuals, spread misinformation or disinformation, and manipulate public opinion. The term "deepfake" is derived from the combination of "deep learning" and "fake".
Deepfakes are created using generative adversarial networks (GANs), which are a type of neural network that can generate new data based on patterns in existing data. GANs consist of two neural networks, a generator and a discriminator, that work together to create new data that is similar to the original data.
Deepfakes have gained significant attention in recent years due to their potential for misuse, particularly in the context of political propaganda, revenge porn, and other forms of cybercrime. They have also raised ethical and legal concerns about the use of synthetic media to deceive and manipulate people.
we’ll generate a deepfake video of a group member speaking like a random man from the internet. We’ll use a machine learning model developed by Aliaksandr Siarohin and others in their paper “First Order Motion Model for Image Animation.” The model is special because it learns the movements from an input video and uses these movements to animate a picture. This makes it more efficient than earlier techniques that required users to supply the model with multiple images.we use google collabs attached above with our requires video and image
Download the model weights (vox-adv-cpk.pth.tar) https://drive.google.com/file/d/15CpncEhYB8jBItzd78hR3bmKnLR_E7Et/view?usp=sharing
Explore the deepfake