Deepfake Detection Project Using Deep-Learning

Deepfake Detection Project Using Deep-Learning

Deepfake Detection Project Description

We have presented a deep fake face recognition system using neural network layers and various algorithms in the python programming language.

Since our model is very bulky in size because we are Cloning various GitHub repositories along with the installation of various tarballs. It is advisable to use google Collaboratory for this project. 

Overview of deepfake detection code

We are importing the first-order model from GitHub into our notebook.

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We have created several directories for our project We are using multiple Python packages such as imageio, bottle, bottle with socket, wsaccel ujson, gevent. 
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We have imported warning packets into our notebook which will suppress the unnecessary warning. And we have to define a function using the camera in which we have passed camera quality as 0.8 as the default quality. Here have also imported some of the JavaScript functions into our notebook. 
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Next, we have considered Three static image URLs from the web which we have told in three different variables.
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We have taken several variables such as center_image1_to_head, crop_image1_to_head, image1_crop_expansion_factor. We have Considered default value of expansion factor as 2.5.
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We have created three tuples in which we are storing the values of all the three parameters for all the images.
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We are also using numpy package along with cv2 and resize cv2 allows us to implement some advanced functionalities related to the webcam. We have also imported the face alignment package into our notebook.
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We have created several functions in our notebook which provide different functionalities let us discuss some of them.

Functions such as create_bounding_box return the stack value that resides in NumPy package. If we talk about functionality, this function mainly deals with the minimum and maximum coordinates of x and y and returns the result.

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Let us discuss another function fix_dims( ), which takes an image as the input parameter IT returns the sliced list.

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For detecting fake images we have a function get crop which takes an image as the parameter and along with some boolean variables & expansion factor which is set to 1 by default. This function mainly deals with X and Y coordinates and returns the cropped image.
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Based on our requirement, we have also imported the request library, regular expression, time, and JSON for our usage.  We have used the various functions in which we are accessing the data from localhost based on the value of the tunnel variable that we have initialized as argo in the beginning.

this project is basically is for detecting fake images

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We have a function normalized KP which returns the normalized value of the KP where we are using a convex hull package to normalize the KP perimeter in deepfake detection project.

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We have also imported torch, cv2, context, face 64, visualizer, etc in our notebook.
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We have a function load style which takes Avatar and crop as the parameters where we have set up drop as 0 to be the default value. This function is adapted from a GitHub repository and mainly deals with the colour code of the image.

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We have another function full normalise KP that returns the complete normalised value. This function where we have used torch packages.

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We have also defined a function that generates animation and which returns the transpose of a numpy array where is the result that we receive after the computation of the image parameters.

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We have also used a sort of socket programming into our notebook we have used pocket route where we have a private software as a parameter, we are using some of the global variables along with some binary variable in deepfake detection project.

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We have used a follow up in which we have used exception handling this basically use exaggeration factor along with other mathematical Parameters.

We have imported logging server adaptor from water package by WCF from gevent package websockethandler and create logger from gevent websocket package.

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We have created a class G event websocket server in which we are passing server adaptor object. This class consists of two functions run( ) and shutdown( ) handler is a perimeter and we are calling WSGI server function which resides in the event websocket.handler.

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Shutdown function is another function that is besides inside this class this function is mainly used to shut down the server.

Lastly, I would like to mention that we are using multiprocessing in our project since, this project has a lot of models and github repositories cloned in it. That makes it very difficult for us to process all the thing on single processor. This is the reason we have used to processes in this.

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We have used apply async function which basically keeps track of the execution of each of each thread. We are using my gevent websocket server class object in which we are passing host and port number and we are running the project.
In this way we have created our project.

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