How to Create & Detect Deepfakes Using Deep Learning

create and detect deepfake using deep learning

Deepfake are becoming a more common occurrence in today’s world. What is deepfake and how can you create it using deep learning? This blog post will help data scientists learn techniques for creating and detecting deepfakes, so they can stay ahead of this technology. A deepfake is a video or audio that alters reality by changing the way something appears. For example, someone could place your face onto someone else’s body in a video to make it seem like you were there when you really weren’t. There are many ways that one can detect if a photo has been manipulated with software such as Photoshop or Gimp.

What is deepfake?

Deepfake is a type of technology that uses machine learning and deep learning to make it seem like one person said something they didn’t, or makes them appear somewhere when they weren’t. It is a form of photoshopping an image without actually using software such as Photoshop.

Deepfake can be used in scenarios such as follow:

  • Placing celebrities in place of other celebrities
  • Altering someone’s face to show them saying something they didn’t say, such as an insult. This is called a deepfake attack and can cause people harm or embarrassment if it were widely available on the internet.    
  • Changing political candidates by altering speeches so that it appears like they said something they didn’t.
  • Changing the content of an image to show them doing something that never happened, such as smoking or drinking alcohol when they actually don’t do those things.
  • When tiny modifications are made to a video or picture, it is difficult to determine whether it has been doctored. Data scientists employ a variety of approaches to establish whether an audio or image has been modified using applications like Photoshop, but these methods wouldn’t work for detecting deepfakes produced using machine learning or deep learning algorithms.

Which deep learning algorithms can be used to create and detect deepfake?

Deep learning algorithms such as a generative adversarial network (GAN) can be used to create deepfakes. Discriminative models can be used as a method of detecting deepfake videos.

Generative adversarial networks (GANs) are an approach to training generative models, in which two neural nets work together to generate fake images that look real and have never been seen before. The first network is called the “generator” and it creates new fakes. The second network is called the “discriminator,” which tries to detect whether the images are real or fake.

The discriminative model can be used as a method of detecting deepfake videos by using adversarial learning techniques for example, where an attacker’s system trains itself on examples of deepfake videos in order to fool a detector.

The attacker’s goal is to create the best possible fake image that can’t be detected as being manipulated with software such as Photoshop or Gimp. The discriminative model’s job is to determine whether an image has been altered using other software so it can stop any harmful information from being spread on the internet.

What are some popular deepfake research papers?

The following is a list of research papers about deepfake:

  • Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks“: This paper focuses on how to generate fake images using a generator and discriminator. The GAN used in this research generated high-quality photos of dogs, people, bedrooms, etc
  • Multi-scale Deep Learning for Face Anti-spoofing“: This paper focuses on how to predict whether an image has been altered using other software such as Photoshop. The discriminative model used in this research predicted if a photo was real or fake with 96% accuracy, and could also detect deepfakes.
  • Deep Photo Style Transfer“: This paper discusses how deep learning can be used to transfer the style of an image onto another photo, such as a famous painting or scenery. The discriminative model discussed in this research determined whether two images were real with 96% accuracy and could also detect deepfakes.
  • A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detecton“: This paper discusses how to use a discriminative model in deepfakes detection where an adversary is used to generate fake images that are very hard to detect as being manipulated with software such as Photoshop. The paper uses a deep convolutional neural network (CNN) for generating high-quality fake images to fool the discriminative model.

What are some good video tutorials on Deepfakes?

There are many video tutorials on deepfakes. Here are few examples:

  • Deepfakes: A New Frontier of Misinformation?”: This video tutorial discusses how machine learning and deep learning are being used to create fake videos. It also shows examples of deepfake videos that have been created such as one showing the former president Barack Obama calling Donald Trump.
  • How to create deepfake using AI“: In this deepfake tutorial for beginners, you’ll discover how to make your own deepfake from scratch, including what a deepfake is, how it works, when you should use it, and how to identify a fake video. You will also learn how to produce deepfakes using hands-on practice at the end of the lesson.
  • Deepfake tutorial – Beginners guide“: In this step-by-step tutorial for beginners, learn how to create or modify a face using DeepFace Lab. A professional deepfake artist offers additional suggestions and hacks.
  • Deepfake Detection tutorial – Step-by-step explanation“: In this video, you will look at how to make a deepfake using Deep Face Lab. You will go through the steps needed to train our model to produce a realistic-looking deepfake using a simple cheat sheet.

What are some opensource libraries for experimenting with deep fake?

There are online tools available that allow users to experiment with deepfake videos. Here are a few examples:

  • FaceSwap GAN: This tool is an open-source project for creating deep fake photos.
  • Face Swap: This tool allows users to swap faces.The algorithm searches for two faces in the frame. After that, it uses dlib face landmarks to determine facial features. The faces are cut out of the picture and the transformation matrix used to transpose one face over the other is estimated using facial landmarks.
  • Deepfake detector: This repository provides the official Python implementation of Unmasking DeepFake with simple Features (Paper: The DFT (Discrete Fourier Transform) method is well-known for its ability to extract information from images, both still and moving. This approach transforms the input data in two stages: via a pre-processing stage, where the input is changed to a domain more convenient for the classifier; and then by a training block, where the input is normalized and then transformed in the domain of the feature.


Creating deep fake images and videos is a new technique that has been used to spread misinformation. With the rise of this technology, it will be important for all people in business and politics to take steps to ensure they are safe from disinformation campaigns. Tools such as GAN models can be used for creating or detecting deep fakes. These tools can help protect against false information being shared online which could harm an organization’s reputation or create confusion among consumers about products on the market. We recommend all organizations start using these techniques so their company continues running smoothly without any threats to its existence!

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog,
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