Deepfake: The Rise of Synthetic Media

The Rise of Synthetic Media – a groundbreaking technology that blurs reality and fiction. Explore its implications and the concerns surrounding AI-powered manipulation in this article.

What is Deepfake

Deepfakes are a sort of synthetic media in which a person’s likeness is substituted in an existing image or video. Deepfakes are made using deep learning, a technology that teaches computers to recognize and repeat patterns in data. Deepfakes are created by training a computer on a large dataset of images or videos of the real person and the person being replaced. Once taught, the computer may be used to produce new photos or videos in which the likeness of the original person has been replaced with the likeness of the other person.

Deepfakes might be utilized for a number of objectives, including entertainment, teaching, and research. They can, however, be used for malicious purposes such as disseminating false information or producing malicious content. As deepfake technology advances, it is critical to be aware of the possible threats and to be able to identify deepfakes.

How to install roop Deepfake

Basic and GPU-powered installs are available.

Basic:

It will most likely operate on your PC, but it will be extremely slow. You can proceed with the basic installation by following the instructions.

1. Installing Python and pip
   • Visit the Python website at https://www.python.org/.
   • Go to the “Downloads” section and get the most recent version of Python for your operating system.
   • To install Python, run the installer and follow the instructions. Check the box to include Python in your system’s PATH variable.
   • To validate the installation of Python, open a command prompt or terminal and execute python –version. It should show the Python version that is currently installed.
   • To install pip, the Python package installer, follow the instructions at https://pip.pypa.io/en/stable/installing/.

2. Installing Git
   • Go to https://git-scm.com/ to see the official Git website.
   • Download the correct Git version for your operating system.
   • To install Git, run the installer and follow the instructions.
   • Open a command prompt or terminal and run git --version to check the installation. It should show the Git version that is currently installed.

2. Installing FFmpeg
   • FFmpeg is a distinct software program that is used to manage multimedia data. Visit the FFmpeg website at https://ffmpeg.org/ and follow the download and installation instructions for your operating system.
   • You should be able to use FFmpeg from the command line after installing it.

2. Installing “Microsoft Visual C++ 14” and C++ build tools (Windows only)
   • If you are using Windows, you may need to install “Microsoft Visual C++ 14” and the C++ build tools.
   • You may get them from the Microsoft website and install them. Search for “Microsoft Visual C++ 14 download” and proceed as directed.
3. Cloning the repository and installing requirements:
   • Navigate to the location where you wish to clone the “roop” repository in a command prompt or terminal.
   • To clone the repository, use the following command:

git clone https://github.com/s0md3v/roop

• After cloning the repository, use the following command to browse to the “roop” directory:

cd roop

• To install the necessary Python packages, use the following command:

pip install -r requirements.txt

6. Downloading the required file:
   • Download the file from one of the specified mirrors (Mirrors #1, #2, #3, and #4).
   • Put the downloaded file in the “roop” folder.
   • Rename the file to “inswapper_128.onnx” if it isn’t already.

After completing these steps, you should have all of the required dependencies and files installed. In order to utilize the program, execute the following command in the “roop” directory:

python run.py

GPU:

If you have a good GPU and are prepared to solve any software difficulties that arise, you may activate GPU, which is much quicker. To do so, first follow the general installation instructions and then the GPU-specific instructions.

For AMD GPUs on Linux:

1. Install the PyTorch and ONNX Runtime packages with the following command:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.4.2

2. Using the following command, uninstall the existing onnxruntime package:

pip uninstall onnxruntime

3. Navigate to the cloned directory of the onnxruntime repository:

git clone https://github.com/microsoft/onnxruntime && cd onnxruntime

4. By using the following instructions, you may build and install ONNX Runtime:

./build.sh --config Release --build_wheel --update --build --parallel --cmake_extra_defines CMAKE_PREFIX_PATH=/opt/rocm/lib/cmake ONNXRUNTIME_VERSION=$ONNXRUNTIME_VERSION onnxruntime_BUILD_UNIT_TESTS=off --use_rocm --rocm_home=/opt/rocm
pip install build/Linux/Release/dist/*.whl

For NVIDIA GPUs on Windows:

1. Download and install the CUDA toolkit from the official website.
2. Download CUDNN (CUDA Deep Neural Network library) from the NVIDIA Developer website.
3. Using the following command, uninstall the existing onnxruntime and onnxruntime-gpu packages:

pip uninstall onnxruntime onnxruntime-gpu

4. Install PyTorch with GPU support using the following commands:

pip install torch torchvision torchaudio --force-reinstall --index-url https://download.pytorch.org/whl/cu118

5. Install the onnxruntime-gpu package:

pip install onnxruntime-gpu

For Mac:

1. Using Homebrew, install the following packages:

brew install wget cmake protobuf git git-lfs

2. Clone the onnxruntime-silicon repository and browse to the cloned directory:

git clone https://github.com/cansik/onnxruntime-silicon && cd onnxruntime-silicon

3. Run the following build script to create ONNX Runtime:

./build-macos.sh

4. Using pip, install the created wheel file:

pip install dist/*whl

For Intel:

1. Uninstall the existing onnxruntime package:

pip uninstall onnxruntime

2. Install the OpenVINO Intel-specific onnxruntime. For installation instructions relevant to your system, please refer to OpenVINO official documentation.

How do I use roop Deepfake

Executing python run.py command will launch this window:

Select a face (the image with the desired face) and the target image/video (the image/video in which you want to replace the face) and press the Start button. Navigate to the location where you want your output to reside in File Explorer. There is a directory called <video_title> where you can witness the frames being switched in Realtime. It will generate the result file after the processing is completed. That’s all. If you don’t know what you’re doing, don’t touch the FPS checkbox.

How to detect deepfake images

Detecting deep fake photos can be difficult as deep fake technology evolves and becomes more sophisticated. However, here are several approaches for identifying deep fake images:

1. Facial and Body Movements: Examine the image’s face and body motions carefully. Deep fake pictures may include abnormal or inconsistent motions, such as a lack of blinking, unusual eye or head movements, or lip-syncing that is not synchronized.
2. Anomalies and Artifacts:Deep fakes can occasionally cause defects or aberrations in an image. Look for strange distortions, unclear edges, or uneven lighting, all of which might suggest manipulation.
3. Inconsistent Background or Foreground: Look for irregularities in the background or foreground items. Deep fake photos could display significant distortions or unusual mixing when modified material meets authentic stuff.
4. Facial and Body Proportions: Examine the proportions of different face features and body components. Deep fake photos may have subtle distortions or exaggerated dimensions that differ from normal human anatomy.
5. Uncanny Valley: Deeply fabricated photos may produce an uncanny valley effect, in which the person’s look appears almost but not quite realistic. If something feels odd or weird about the visual, trust your instincts.
6. Source and Context: Consider the image’s origins and context. If it originates from an unconfirmed or suspicious source, or if the image is very contentious or dramatic, it is worth investigating further.
7. Metadata Analysis: Examine the image’s metadata, which includes information about the camera, date, and location. Deep fake photos may include changed information or variations that provoke suspicion in some circumstances.
8. Reverse Image Search: Use tools like Google Images or TinEye to perform a reverse image search to determine whether the picture appears elsewhere on the internet. Because deep fake pictures are frequently made from existing photos, discovering several occurrences of the same image may suggest manipulation.

It’s important to note that these methods may not be foolproof, as deep fake technology continues to advance. As a general rule, it is important to be cautious and critically assess pictures, particularly those that appear suspicious or too good to be true.

Also Read: DragGAN: : The AI-Powered Image Editing Tool That Makes Editing Images Easy

This article is to help you learn Deepfake. We trust that it has been helpful to you. Please feel free to share your thoughts and feedback in the comment section below.