DeepFake with Python

Creating deepfakes involves a combination of machine learning, computer vision, and image processing. It's important to note that generating deepfakes can have ethical and legal implications, and their creation or distribution should always comply with applicable laws and ethical guidelines. Furthermore, deepfake technology can be misused, so it is crucial to approach it responsibly.

The creation of deepfake content typically involves several steps, including data collection, model training, and image/video manipulation. However, given the sensitivity of the subject matter (creating fake news, manipulating images/videos of real individuals), I strongly advise against creating or distributing misleading content, as it can harm people's reputations, mislead the public, or violate privacy and intellectual property laws.

That being said, here’s an explanation of the general procedure to create a deepfake using a Python framework and a high-level overview of the tools typically used in such projects. **This is for educational purposes only** and **does not include any specific deepfake content**.

### Steps for Generating Deepfake Content:

1. **Data Collection**:

   - Collect video or images of the boy standing and walking, preferably with the same lighting and background.

   - Collect an image or video of an old newspaper with a "Chandigarh publication" (for the purpose of creating a fake news scenario).

   

2. **Preprocessing**:

   - Use computer vision techniques to align the images and prepare them for processing (e.g., cropping faces, adjusting image sizes, etc.).

3. **Training the Deepfake Model**:

   - You would typically use a Generative Adversarial Network (GAN) or a pre-built deepfake tool to create a synthetic face or movement.

   

4. **Post-processing**:

   - Merge the generated deepfake video with the newspaper image to simulate the boy interacting with the fake news.

For the deepfake part, **DeepFaceLab** and **Faceswap** are popular tools, but they require a lot of computation resources, data, and time to train a model. Using pre-trained models (like **StyleGAN2** or **DeepFake TensorFlow models**) can help you avoid training from scratch.

---

### Example Code Structure

Here’s an outline of how you might create a simple deepfake using Python with tools such as OpenCV, TensorFlow, and `deepfake` libraries (you would need to install various dependencies for this).

1. **Install Required Libraries**:

   ```bash

   pip install opencv-python numpy tensorflow keras dlib moviepy

   ```

2. **Load the Dataset (Images or Videos)**:

   You need video or image inputs for both the "boy" and the "old newspaper". For simplicity, we will assume you have pre-collected these.

3. **Deepfake Process with OpenCV (basic example)**:

   Here’s a very basic concept of replacing the boy’s face with a synthetic one (you would need a trained model to generate realistic outputs):

```python

import cv2

import numpy as np

# Load pre-trained face recognition models (using OpenCV's Haar Cascade Classifier as an example)

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Load the video of the boy walking (the source video)

video_capture = cv2.VideoCapture('boy_walking_video.mp4')

# Load the old newspaper image (target image for the deepfake effect)

newspaper_image = cv2.imread('chandigarh_newspaper.jpg')

while True:

    # Read the current frame from the video

    ret, frame = video_capture.read()

    if not ret:

        break

    # Convert frame to grayscale (for face detection)

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect faces in the video frame

    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    # For each detected face, overlay the "newspaper" image

    for (x, y, w, h) in faces:

        # Resize the newspaper image to fit the detected face area

        newspaper_resized = cv2.resize(newspaper_image, (w, h))

        # Replace the face region with the resized newspaper image

        frame[y:y+h, x:x+w] = newspaper_resized

    # Display the resulting frame

    cv2.imshow('Deepfake Simulation', frame)

    # Press 'q' to exit the video window

    if cv2.waitKey(1) & 0xFF == ord('q'):

        break

# Release the video capture and close windows

video_capture.release()

cv2.destroyAllWindows()

```

### Explanation of Code:

- **Face Detection**: The code uses OpenCV’s Haar Cascade classifier to detect faces in each video frame.

- **Image Overlay**: For each detected face, it overlays the "Chandigarh newspaper" image, resizing it to fit the detected face dimensions. This would simulate the deepfake effect (though not a true deepfake, this is more of an overlay).

- **Video Processing**: The code reads each frame from the input video and applies the transformation in real time.

### Advanced Deepfake Techniques:

To create a true deepfake (i.e., synthesizing a boy’s face or body in motion), you'd need:

1. **Deepfake Models (GANs, Autoencoders)**: These models generate synthetic faces or entire videos based on trained datasets. Tools like **DeepFaceLab** or **Faceswap** can be used.

2. **Pre-trained Models**: You could use pre-trained models like **StyleGAN2** for generating synthetic faces or **DeepFake** tools for animating faces.

3. **Generative Adversarial Networks (GANs)**: Used for generating realistic fake images or videos, where a "generator" creates synthetic content, and a "discriminator" tries to detect whether the content is real or fake.

4. **Optical Flow and Motion Synthesis**: For adding walking or body movements, you could combine motion synthesis models like **OpenPose** or use **3D GANs**.

### Ethical Considerations and Warning:

- **Ethical Issues**: Creating deepfakes for malicious purposes, such as spreading fake news, is unethical and illegal in many jurisdictions. You should always respect privacy and avoid using deepfake technology to deceive or harm others.

- **Consent**: Ensure that you have consent from individuals whose images or likenesses you are using, and ensure that the content you create doesn’t violate intellectual property or media rights.

I strongly encourage using this technology responsibly and only for educational, research, or entertainment purposes, with a clear understanding of the risks involved.

----------------------------------

Automatic deepfake creation software has evolved significantly in recent years, and many tools are now available that can generate deepfakes (synthetic media such as manipulated images, videos, and audio). While these tools are often used for creative or educational purposes, it is crucial to be aware of the ethical and legal considerations, especially when creating or distributing deepfake content.

Here’s a list of some of the most popular and powerful **automatic deepfake creation software**:

### 1. **DeepFaceLab**

**DeepFaceLab** is one of the most advanced and popular tools for creating deepfakes. It allows users to swap faces in videos and images, and it provides fine control over the process, making it popular among deepfake creators.

- **Key Features**:

  - Face swapping in videos (including full-face replacement).

  - Supports high-resolution video output.

  - Can work with both faces and bodies.

  - Highly customizable and offers several neural network architectures.

  - A wide range of pre-configured models for face recognition, alignment, and swapping.

  

- **How it Works**:

  - DeepFaceLab allows you to train models using a dataset of images or videos of the source and target faces.

  - It offers a GUI as well as command-line tools for more advanced users.

  

- **Platform**: Windows (can run on Linux with additional configuration).

- **Link**: [DeepFaceLab GitHub](https://github.com/iperov/DeepFaceLab)

### 2. **Faceswap**

**Faceswap** is another popular deepfake software that offers a full-featured GUI, making it easier for beginners and advanced users alike to create deepfakes.

- **Key Features**:

  - Open-source software for face swapping in images and videos.

  - Supports face training with different types of neural networks (e.g., VAE, GAN).

  - Works with a range of formats, including high-resolution images and videos.

  - Includes support for face alignment, recognition, and other important tasks.

  

- **How it Works**:

  - Faceswap allows you to collect data (images/videos of faces you want to swap) and train a model.

  - Once the model is trained, it can be used to swap faces across videos.

  

- **Platform**: Windows, macOS, and Linux.

- **Link**: [Faceswap GitHub](https://github.com/deepfakes/faceswap)

### 3. **Zao**

**Zao** is a mobile app that allows users to create deepfakes in real time, typically by swapping faces into existing videos, like movie scenes or viral clips. While it offers automatic face-swapping, it is very limited compared to software like DeepFaceLab or Faceswap in terms of customization and video editing.

- **Key Features**:

  - Instant face-swapping into famous video clips.

  - Easy to use with just a smartphone camera.

  - Real-time processing with AI-driven face detection and replacement.

  

- **How it Works**:

  - You upload a photo, and the app automatically places your face in a selected video clip, allowing you to create deepfakes quickly.

  

- **Platform**: iOS (not available in all countries).

  

- **Link**: [Zao App](https://apps.apple.com/us/app/zao/id1460631350) (App Store)

### 4. **Reface (formerly Doublicat)**

**Reface** is a popular deepfake app for mobile devices that specializes in face-swapping. It's primarily focused on swapping faces into GIFs or short video clips but offers a very intuitive user experience.

- **Key Features**:

  - Automatic face-swapping into GIFs, videos, or images.

  - Simple to use — users only need to upload a selfie.

  - Fast processing with high-quality results.

  - Fun for entertainment (e.g., face-swapping into famous movie scenes, celebrities, etc.).

  

- **How it Works**:

  - Reface uses AI to map your face onto the face in the video or GIF you select, and the app automatically generates the deepfake video.

  

- **Platform**: iOS and Android.

  

- **Link**: [Reface App](https://reface.app/)

### 5. **Avatarify**

**Avatarify** allows users to create deepfakes in real-time, where you can animate static photos with your own facial expressions. It uses a pre-trained model to make avatars (e.g., celebrities, historical figures) come to life.

- **Key Features**:

  - Real-time face animation.

  - Supports avatars and famous faces (e.g., Mona Lisa, Albert Einstein).

  - Can integrate with video calls, making avatars appear live during meetings.

  - Easy to use and lightweight.

- **How it Works**:

  - Avatarify maps your facial expressions onto a pre-existing static image or avatar.

  - You can use it for video calls or to create content with famous avatars.

- **Platform**: Windows, macOS, and Linux.

- **Link**: [Avatarify GitHub](https://github.com/alievk/avatarify)

### 6. **Synthesia**

**Synthesia** is a more professional, AI-driven platform for creating deepfake videos, and it is often used in business contexts (e.g., corporate videos, advertisements). It allows you to generate realistic videos with AI avatars that can speak in multiple languages.

- **Key Features**:

  - Create AI-generated avatars to present scripts in multiple languages.

  - Real-time video creation with AI avatars.

  - Offers pre-built templates and avatars.

  - No need for professional video production skills.

- **How it Works**:

  - You can create an avatar, input your script, and Synthesia’s AI will generate a video of your avatar reading the script.

  

- **Platform**: Web-based.

- **Link**: [Synthesia](https://www.synthesia.io/)

### 7. **Artbreeder**

While **Artbreeder** is not primarily a deepfake tool, it allows users to generate highly realistic portraits or images by blending various faces. This could be used for generating realistic faces or avatars, which you could use in your deepfake videos.

- **Key Features**:

  - Image blending and manipulation using AI.

  - Create realistic faces by combining features from different images.

  - Users can control features like age, ethnicity, and expression.

  

- **How it Works**:

  - Users can adjust sliders to generate or manipulate faces, or combine different faces to create new ones.

  

- **Platform**: Web-based.

- **Link**: [Artbreeder](https://www.artbreeder.com/)

---

### Ethical and Legal Considerations:

While these tools provide powerful ways to create deepfake content, it's important to use them responsibly:

- **Consent**: Always ensure you have permission to use the likeness of others, especially in a video. Using someone’s face or voice without their permission can violate privacy rights.

- **Misuse and Harm**: Creating deepfakes to deceive or harm others is unethical and could have legal consequences. Always consider the impact of the content you create.

- **Laws**: Different countries have varying laws regarding the creation and distribution of deepfakes. In many places, creating deepfakes for malicious purposes (e.g., fake news, harassment, fraud) is illegal.

### Conclusion:

Automatic deepfake creation software has made significant advancements, and many of the tools listed above make it easier to generate deepfake videos and images. However, due to the potential for misuse, it's essential to approach this technology with caution, and always adhere to ethical standards and legal guidelines when creating or sharing deepfake content.

-------------------------

DeepFake is composed of Deep Learning and Fake means taking one person from an image or video and replacing it with someone else likeness using technology such as Deep Artificial Neural Networks [1].

Kaggle dataset is used https://www.kaggle.com/c/deepfake-detection-challenge/data

Github Reference: https://github.com/ageitgey/face_recognition

!pip install face_recognition

Data

• The data is comprised of .mp4 files, split into ~10GB apiece. A metadata.json accompanies each set of .mp4 files and contains the filename, label (REAL/FAKE), original and split columns, listed below under Columns.
• The full training set is just over 470 GB.

References: https://deepfakedetectionchallenge.ai/faqs

Data exploration

DATA_FOLDER = '../input/deepfake-detection-challenge'
TRAIN_SAMPLE_FOLDER = 'train_sample_videos'
TEST_FOLDER = 'test_videos'

print(f"Train samples: {len(os.listdir(os.path.join(DATA_FOLDER, TRAIN_SAMPLE_FOLDER)))}")
print(f"Test samples: {len(os.listdir(os.path.join(DATA_FOLDER, TEST_FOLDER)))}")

Files

• train_sample_videos.zip
• sample_submission.csv
• test_videos.zip

Metadata Columns

• filename - the filename of the video
• label - whether the video is REAL or FAKE
• original - in the case that a train set video is FAKE, the original video is listed here
• split - this is always equal to “train”.

Check files type

train_list = list(os.listdir(os.path.join(DATA_FOLDER, TRAIN_SAMPLE_FOLDER)))
ext_dict = []
for file in train_list:
    file_ext = file.split('.')[1]
    if (file_ext not in ext_dict):
        ext_dict.append(file_ext)
print(f"Extensions: {ext_dict}")

Video data exploration

We check first if the list of files in the meta info and the list from the folder are the same.

meta = np.array(list(meta_train_df.index))
storage = np.array([file for file in train_list if  file.endswith('mp4')])
print(f"Metadata: {meta.shape[0]}, Folder: {storage.shape[0]}")
print(f"Files in metadata and not in folder: {np.setdiff1d(meta,storage,assume_unique=False).shape[0]}")
print(f"Files in folder and not in metadata: {np.setdiff1d(storage,meta,assume_unique=False).shape[0]}")

Example of Fake video aagfhgtpmv.mp4

Let's use the face_recognition package to detect faces in the video

Check out this great kernel here https://www.kaggle.com/brassmonkey381/a-quick-look-at-the-first-frame-of-each-video for how I learned to capture a frame from the video file.

import cv2 as cv
import os
import matplotlib.pylab as plt
train_dir = '/kaggle/input/deepfake-detection-challenge/train_sample_videos/'
fig, ax = plt.subplots(1,1, figsize=(15, 15))
train_video_files = [train_dir + x for x in os.listdir(train_dir)]
# video_file = train_video_files[30]
video_file = '/kaggle/input/deepfake-detection-challenge/train_sample_videos/akxoopqjqz.mp4'
cap = cv.VideoCapture(video_file)
success, image = cap.read()
image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
cap.release()   
ax.imshow(image)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
ax.title.set_text(f"FRAME 0: {video_file.split('/')[-1]}")
plt.grid(False)

Now, use OpenCV to detect the faces using the face_recognition package! First, we need to pip install it. Make sure you have internet turned on in your kernel.

Reference: https://github.com/ageitgey/face_recognition

Frame by Frame Face Detection

• First we will loop through the frames of the video file and append them to a list called frames

video_file = '/kaggle/input/deepfake-detection-challenge/train_sample_videos/akxoopqjqz.mp4'

cap = cv2.VideoCapture(video_file)

frames = []
while(cap.isOpened()):
    ret, frame = cap.read()
    if ret==True:
        frames.append(frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    else:
        break
cap.release()

print('The number of frames saved: ', len(frames))