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How to Build Deepfake Protection Software

How to Build Deepfake Protection Software

Technology has facilitated huge transformations in our global society. However, not all of them have been positive. Artificial Intelligence (AI) arguably has the biggest transformative potential of any technology. However, one potentially devastating use of AI is deepfakes.

Governments, businesses, and other organizations are going to increasingly want to prevent deepfakes from causing havoc. Deepfake prevention is going to be a multi-billion-dollar-a-year industry with a decade or two.

Are you wanting to be one of the first to capitalize on this future market and so are wondering how to build a deepfake protection software? In this article, we are going to explain how you can do just that.

DevTeam.Space has build numerous AI and ML solutions and so know just what goes into building and training an artificial intelligence solution.

Here are some of our recent projects:

  1. HitFactor: DevTeam.Space created this app that uses a neural network to analyze videos.
  2. A Neural Network Library: A team of DevTeam.Space ML (Machine Learning) developers created a library for neural network construction and compiler optimization.
  3. A High-Speed Vehicle Identification System: A development team from DevTeam.Space created a set of neural networks for identifying car number plates in a video stream.

Read on, as we will now explain how to create a deepfake protection software.

A Brief Introduction to Deepfakes

Fake news isn’t new. Purveyors of fake news use various tools, e.g., false media reporting, manipulated images on social media, etc. Governments, businesses, media watchdogs, and other bodies have come up with various ways to identify and expose fake news.

However, deepfakes represent a powerful new way to compound the problem of fake news though by slipping past traditional detection methods.

As a PCMag report states, reports on deepfakes only emerged as recently as 2017. Deepfake technology is a form of AI. It can create fake video content that will show events that never happened. In other words, deepfake videos are synthetic media created using AI.

The most recent example saw The Queen of England giving an alternative Christmas speech.

Fighting Deepfakes

Deepfake tools use the same AI technologies to identify faked videos. These tools use video analysis and large datasets containing videos of the targeted person or persons to establish whether a video is genuine or not.

Deepfake AI applications can be trained to accurately spot deepfakes in a matter of seconds.

While expert observers or analysts could easily spot deepfake videos, the more accurate they get the harder they will be to spot with the untrained eye.

Since viral videos only take a matter of hours to spread and do their damage, the need for fast AI deepfake tools is extremely important if we are to successfully fight deepfakes.

Deepfake tools can achieve this by using better AI algorithms and bigger datasets to identify even the most authentic looking fakes.

The deepfake technology uses several branches of AI, e.g.:

  • Deep Learning (DL);
  • Neural networks;
  • Generative Adversarial Networks (GANs).

It also uses several techniques. The most common ones among them are:

  • “Lip-sync”;
  • “Face-swap”;
  • “Puppet-master”.

These techniques combine video and audio from one set of videos with the images in the other set of videos.

The Threats from Deepfake Technology

One can access the deepfake technology relatively easily. E.g., Faceswap is a free and open-source deepfakes software enabling face-swapping.

The easy availability of deepfake tools poses serious threats, e.g.:

  • Purveyors of fake news can use deepfake videos to launch misinformation campaigns. E.g., they can accuse governments, businesses, or other organizations of scams that never took place.
  • Cybercriminals can create fake videos using deepfake tools to harm the reputation of celebrities. They can use these videos to demand ransoms.
  • Political propagandists with questionable practices can create fake videos to harm the reputation of politicians. They can make these viral on social media platforms. Deepfake videos targeting prominent political figures like Barack Obama, Donald Trump, and Nancy Pelosi have already emerged. 
  • Political actors with questionable objectives can launch massive disinformation campaigns using deepfake videos. Such disinformation campaigns on popular social media platforms can democratic elections like the US presidential election.
  • Cybercriminals can create convincing deepfakes to persuade people to part with sensitive information like identity and biometric information. This augments their strategy to launch mass-scale cyber-attacks using other methods like social engineering, phishing, ransomware, etc. Experts in fields like cybersecurity and antivirus have already warned about “deepfake ransomware”

Additionally, creators of deepfake videos use the people’s personal information, e.g., images from drivers’ licenses. They often violate data privacy rules and regulations to obtain this personal information. That’s an unfortunate incentive to cause more data breaches!  

Examples of Deepfake Detection Solutions

Governments, businesses, and other organizations rate the threat from deepfake as high, and they are trying to build deepfake detection technologies. Consider the following examples:

  • In the USA, DARPA (Defense Advanced Research Projects Agency) is building deepfake prevention technologies. The agency is “teaching” computers how to detect errors in deepfake videos. This will help these computers to spot deepfake videos. 
  • Shruti Agarwal and Hany Farid, two researchers at the University of California (UC) Berkley are developing a deepfake detection solution. It utilizes anomalies in facial expressions and style of talking in deepfake videos to identify deepfakes.
  • Some experts believe that deepfake detection solutions should verify real videos and images, which should be their starting point. Factom, a blockchain start-up uses blockchain to verify content from trusted sources. This will make the work of identifying deepfake audio and video content easier.
  • Microsoft has launched Microsoft Video Authenticator in September 2020. This deepfake detection solution can analyze videos and images to provide a percentage chance or confidence score. This score indicates the likelihood of a media being artificially manipulated.

How you can build a Deepfake Protection Software

We suggest you take the following steps to build a deepfake protection software:

1. Define the outline of a solution to spot deepfake content

Form a team with a competent project manager (PM), an experienced software architect, and a team of business analysts. This team should define the functionality of the proposed software system.

It should also define the non-functional requirements (NFRs) like performance, scalability, etc. You should plan the target platforms, e.g., web, Android, iOS, etc.

We recommend you consider two technical approaches to this problem and your solution:

(a) Artificial Intelligence (AI)

You can use AI and ML to “train” a software system on how to spot synthetic media. Use ML algorithms to do this. A few popular types of ML algorithms are as follows:

  • Artificial Neural Networks (ANNs);
  • Naïve Bayes Classifier Algorithms;
  • Support Vector Machine Learning Algorithms;
  • K-Means Clustering Algorithms;
  • K-Nearest Neighbors Algorithms;
  • Decision Tree Machine Learning Algorithms.

The software architect in your team should identify the appropriate type of algorithm

(b) Blockchain

You can also utilize blockchain to create a system that ‘stamps’ all kinds of media to prove its authenticity. This will enable your proposed software system to facilitate immediate rebuttal of fake videos with an immutable timestamp of when and where the video was recorded. This record can be immediately provided to media outlets such as YouTube to ensure the video is immediately taken down and replaced with the original.

This system can also help the AI detection systems mentioned in part A to identify real videos.

As we have explained in our guide to blockchain, this new technology offers the following:

  • Decentralization: The decentralized nature of blockchain networks makes intermediaries irrelevant.
  • Distributed ledger: The distributed ledger of blockchain makes real-time information sharing easier.
  • Immutability: The security features of blockchain, e.g., digital signatures, encryption, cryptographic hash functions, and consensus algorithms prevent tampering with data. Data stored on a blockchain network is mathematically verified. You can confidently proclaim the authenticity of the media content that you store on a blockchain network.
  • Efficiency: Blockchain networks like Ethereum offer smart contracts. You can code contractual terms and conditions in these open-source programs. They execute autonomously, and you can’t reverse their execution. Smart contracts make contract administration easier.

2. Plan the project to build a Deepfake protection software

Plan the project effectively. As we have explained in our guide to the AI software development lifecycle, you should plan for the following:

  • Deciding what to automate;
  • Identifying the AI capabilities, e.g., ML;
  • Creating the best algorithms;
  • Selecting relevant datasets to train the AI/ML algorithms;
  • Managing requirements;
  • Designing the technical solution;
  • Coding, testing, and deployment;
  • Maintenance including post-deployment support.

Consider the following to plan the blockchain software development part:

  • Deciding whether you need a cryptographic token;
  • Choosing between public vs private blockchain networks;
  • Selecting blockchain development platform and framework;
  • Planning for the blockchain network infrastructure.

This proposed blockchain system will record the authenticity of media content, therefore, you don’t need a cryptographic token. You don’t need a public blockchain. We recommend you use Hyperledger Fabric, a popular enterprise blockchain framework. 

3. Use the appropriate cloud platform

You should consider using a cloud platform to expedite your project. An MCSP (Managed Cloud Services Provider) takes care of the provisioning of servers and computational resources. Such platforms have a number of benifits, specifically, the vast computing resources they control and already in-house developed AI expertise and software.

Also, MCSPs manage the cloud platform. This allows you to focus on software development.

You can choose one from several popular MCSPs, e.g.:

  • AWS (Amazon Web Services);
  • Microsoft Azure;
  • Google Cloud Platform.

Different types of cloud platforms exist, e.g.:

  • IaaS (Infrastructure-as-a-Service): MCSPs manage the cloud infrastructure. You take care of application development-related matters. Amazon EC2 (Elastic Compute Cloud) is an example.
  • PaaS (Platform-as-a-Service): In the case of PaaS platforms, MCSPs manage the cloud infrastructure, operating system, middleware, runtime environment, and services line databases. You bring your code and data. AWS Elastic Beanstalk is a PaaS platform.
  • MBaaS (Mobile-Backend-as-a-Service): Several MCSPs offer MBaaS platforms that manage the cloud infrastructure and persistent storage. This makes the development and management of the mobile backend easier. You can focus on the front-end development. AWS Amplify is an MBaaS platform.

Choose a type of cloud platform that caters to your requirements. Keep in mind that Azure and AWS have huge amounts of AI experience and may well have existing open-source AI software that you can use as the basis for your application.

Building an enterprise blockchain from scratch involves plenty of work. Infrastructure management tasks can take a lot of your time, however, BaaS (Blockchain-as-a-Service) platforms can help.

Reputed cloud computing giants provide BaaS platforms that manage that blockchain infrastructure like servers and networking. We have reviewed popular BaaS platforms and found the following ones worth checking out: 

  • Blockchain on AWS;
  • SAP Cloud Platform BaaS;
  • Microsoft Blockchain on Azure;
  • IBM Blockchain platform.

4. Choose the right technology stack to build a deepfake protection software

Your choice of the technology stack depends on your project requirements, e.g.:

  • AI/ML programming: If you plan to code all AI/ML algorithms from scratch, consider using Python. This powerful open-source language suits AI/ML programming.
  • Databases: You can use MySQL or PostgreSQL since both are feature-rich RDBMSs (Relational Database Management Systems). Use MongoDB or Apache Cassandra if you need a NoSQL database.
  • Mobile development: If you want to develop native Android and iOS apps, then you should use the platform-specific programming language. You need to use Java or Kotlin for native Android development. Use Swift or Objective-C for native iOS development. If you prefer hybrid app development, then you can use React Native.
  • Enterprise blockchain development: We had earlier recommended you use Hyperledger Fabric. Smart contracts on Fabric are called “chaincodes”. You can use Node.js, a popular open-source runtime environment for JavaScript to develop chaincodes on Fabric.

5. Hire a software development team

You now need to hire competent people for the following roles:

  • UI designers;
  • Web developers;
  • Mobile developers, if you plan to offer mobile apps;
  • AI/ML developers;
  • Blockchain developers with Hyperledger Fabric skills;
  • Testers;
  • DevOps engineers.

You might wonder whether you should hire freelancers. Freelance platforms might enable you to hire developers at a low hourly rate. However, you might face challenges to manage the work of part-time freelancers. You will need to hire replacement developers if freelancers leave in the middle of your project.

We recommend hiring competent software developers from reputed software development companies. These companies offer management support. They provide replacement if developers leave the project mid-way.

6. Execute, monitor, and control the project

We recommend you build a Scrum team for smooth execution and effective tracking of the project. Your PM can lead the team as the “Scrum Master”. Scrum teams are small, cross-functional teams. Developers, testers, DevOps engineers work together in such a team.

Scrum teams focus on delivering sustained value to clients by fostering collaborations. These teams schedule features in iterations called “Sprints”, and they conduct “Sprint planning meetings” for this. They use processes like “daily stand-up meetings” to resolve project issues promptly. 

Final Thoughts

We have discussed in our overview how to build deepfake protection software.

Keep in mind that such projects involve niche technologies, which are on the frontier of software development at the current time. You will need expertise in artificial intelligence development, machine learning, data science, blockchain, and cutting edge languages like Python.

If you don’t have all the required skill sets and experience then contact DevTeam.Space so you can access our expertise building such projects.

FAQs

1. Do I need experienced developers to create a deepfake detection system?

As we recommended, deepfake protection software should use cutting-edge technologies like AI, ML, and blockchain. These are niche skills. Blockchain is a new technology too. Projects involving such skills tend to be complex. Therefore, you need experienced developers.

2. Can DevTeam.Space provide AI/ML and blockchain developers to build software that detects deepfakes?

DevTeam.Space has worked on multiple AI/ML and blockchain software development projects. We can provide AI/ML and blockchain developers. Our software developers have considerable skills and experience in projects involving these skills.

3. I want a complete software development team and not just developers. Can DevTeam.Space help?

DevTeam.Space can provide individual developers and complete software development teams. We do that for all key skillsets including AI/ML and blockchain. Our software development teams work cohesively, therefore, you don’t need to integrate work from multiple developers.

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