Vaughn  Sauer

Vaughn Sauer

1622814900

Create Deepfakes in 5 Minutes with First Order Model Method

Deepfakes have entered mainstream culture. These realistic-looking fake videos, in which it seems that someone is doing and/or saying something even though they didn’t, went viral a couple of years ago. Today artists and bands, like Steven Wilson and Abrahadabra, are using these techniques to create videos for their songs. Also, applications like ZaoFaceSwap and Reface are providing a way to quickly create various videos. Of course, if you are willing to share your information with them.

In fact, the term first appeared back in 2017, when Motherboard published an article on AI-manipulated porn that appeared to feature actress Gal Gadot. Today, even if you see a video of some celebrity or politician saying something in a video, you will take it with a grain of suspicion (or at least you should do so). “Putting words in someone’s mouth” got a whole new connotation.

Of course, deepfakes raised big ethical and moral concerns, but that didn’t stop us from improving them and technologies to build them. Creating deep fakes in the past was not an easy task, however with recent advances it became a five-minute job. In this tutorial, we will explore how deepfakes are created and we apply a First Order Modeling method, which allows us to create deep fakes in a matter of minutes.

In this article you will learn:

  1. How are Deepfakes Created?
  2. First Order Motion Model for Image Animation
  3. Building your own Deepfake
  4. Use Cases

The Video version of this article can be seen below:

1. How are Deepfakes Created?

The basis of deepfakes, or image animation in general, is to combine the appearance extracted from a source image with motion patterns derived from a driving video. For these purposes, deepfakes use deep learning, where their name comes from (deep learning + fake). To be more precise, they are created using the combination of autoencoders and GANs.

Autoencoder is a simple neural network, that utilizes unsupervised learning (or self-supervised if we want to be more accurate). They are called like that because they automatically encode information and usually are used for dimensionality reduction. It consists of three parts: encoder, code, and decoder.

The encoder will process the input, in our case input video frame, and encode it. This means that it transforms information gathered from it into some lower-dimensional latent space – the code. This latent representation contains information about key features like facial features and body posture of the video-frame. In layman terms, here we have information about what face is doing, does it smile or blinks, etc. The decoder of autoencoder restores this image from the code and uses it for network learning.

Generative Adversarial Networks or GANs are one very cool deep learning concept. Essentially, they are composed of two networks that are competing against each other. The first network tries to generate images that are similar to the training set and it is called the generator. The second network tries to detect where does the image comes from, the training set, or the generator and it is called – the discriminator. Both networks are trying to be better than the other and as a result, we get better-generated images. Learn more about Autoencoders and GANs here.

The problem in the past, when it comes to building deepfakes, was that we needed some kind of additional information, ie. these techniques required some priors. For example, if we wanted to map head movement we would need needed facial landmarks. If we wanted to map full-body movement we needed to do pose-estimation.

That changed at the last year’s NeurIPS conference where the paper “First Order Motion Model for Image Animation” by the research team from the University of Toronto (Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci and Nicu Sebe) was introduced. This method doesn’t require additional information about the subject of animation. Apart from that, once this model is trained, we can use it for transfer learning and apply it to an arbitrary object of the same category.

2. First Order Motion Model for Image Animation

Let’s explore a bit how this method works. The whole process is separated into two parts: Motion Extraction and Generation. As an input the source image and driving video are used. Motion extractor utilizes autoencoder to detect keypoints and extracts first-order motion representation that consists of sparse keypoints and local affine transformations. These, along with the driving video are used to generate dense optical flow and occlusion map with the dense motion network. Then the outputs of dense motion network and the source image are used by the generator to render the target image.

This work outperforms state of the art on all the benchmarks. Apart from that it has features that other models just don’t have. The really cool thing is that it works on different categories of images, meaning you can apply it to face, body, cartoon, etc. This opens up a lot of possibilities. Another revolutionary thing with this approach is that now you can create good quality Deepfakes with a single image of the target object, just like we use YOLO for object detection.

If you want to find out more about this method, check out the paper and the code.

3. Building your own Deepfake

The code for First Order Motion Model can be downloaded from here if you have a fast GPU and want to test this app on your own environment. It contains several important folders and files. For example, within the config folder, you can find configurations for different pre-trained models. The modules folder contains an implementation of mentioned keypoint detector, discriminator, and generator. Also in the root folder, you can find demo.py, which shows how to use this repository and even train.py. The pre-trained model can be downloaded from here.

In order to use this solution, you need to download a pre-trained model. So, we are utilizing Transfer Learning to do this. For this purpose we utilize _vox-adv-cpk.pth.tar, _ which is a model for face animation.

Once you downloaded it, put it in the root of the repository. In order to use this solution, you need to download a pre-trained model. So, we are utilizing Transfer Learning to do this. Once you downloaded it, put it in the root of the repository.

To to make your deepfake video follow these steps:

  • Record driving video that you want to use and pick an image that you want to use

  • Run following command to crop the video:

  • Run this command to create deepfake video with proper parameters:

You can do so by following this Collab notebook. In essence, what you need to do is clone the repository and mount your Google Drive. Once that is done, you need to upload your image and driving video to drive. Make sure that image and video size contains only face, for the best results. Use ffmpeg to crop the video if you need to. Then all you need is to run this piece of code:

Here is my experiment with image of Nikola Tesla and a video of myself:

3. Use Cases

There are several interesting music videos done by this technique. Check them out:

Conclusion

We are living in a weird age in a weird world. It is easier to create fake videos/news than ever and distribute them. It is getting harder and harder to understand what is truth and what is not. It seems that nowadays we can not trust our own senses anymore. Even though fake video detectors are also created, it is just a matter of time before the information gap is too small and even the best fake detectors can not detect if the video is true or not. So, in the end, one piece of advice – be skeptical. Take every information that you get with a bit of suspicion because things might not be quite as it seems.

Thank you for reading!

#ai #python #artificaial inteligance #artificial neural networks #computer vision #data science

What is GEEK

Buddha Community

Create Deepfakes in 5 Minutes with First Order Model Method
Vaughn  Sauer

Vaughn Sauer

1622814900

Create Deepfakes in 5 Minutes with First Order Model Method

Deepfakes have entered mainstream culture. These realistic-looking fake videos, in which it seems that someone is doing and/or saying something even though they didn’t, went viral a couple of years ago. Today artists and bands, like Steven Wilson and Abrahadabra, are using these techniques to create videos for their songs. Also, applications like ZaoFaceSwap and Reface are providing a way to quickly create various videos. Of course, if you are willing to share your information with them.

In fact, the term first appeared back in 2017, when Motherboard published an article on AI-manipulated porn that appeared to feature actress Gal Gadot. Today, even if you see a video of some celebrity or politician saying something in a video, you will take it with a grain of suspicion (or at least you should do so). “Putting words in someone’s mouth” got a whole new connotation.

Of course, deepfakes raised big ethical and moral concerns, but that didn’t stop us from improving them and technologies to build them. Creating deep fakes in the past was not an easy task, however with recent advances it became a five-minute job. In this tutorial, we will explore how deepfakes are created and we apply a First Order Modeling method, which allows us to create deep fakes in a matter of minutes.

In this article you will learn:

  1. How are Deepfakes Created?
  2. First Order Motion Model for Image Animation
  3. Building your own Deepfake
  4. Use Cases

The Video version of this article can be seen below:

1. How are Deepfakes Created?

The basis of deepfakes, or image animation in general, is to combine the appearance extracted from a source image with motion patterns derived from a driving video. For these purposes, deepfakes use deep learning, where their name comes from (deep learning + fake). To be more precise, they are created using the combination of autoencoders and GANs.

Autoencoder is a simple neural network, that utilizes unsupervised learning (or self-supervised if we want to be more accurate). They are called like that because they automatically encode information and usually are used for dimensionality reduction. It consists of three parts: encoder, code, and decoder.

The encoder will process the input, in our case input video frame, and encode it. This means that it transforms information gathered from it into some lower-dimensional latent space – the code. This latent representation contains information about key features like facial features and body posture of the video-frame. In layman terms, here we have information about what face is doing, does it smile or blinks, etc. The decoder of autoencoder restores this image from the code and uses it for network learning.

Generative Adversarial Networks or GANs are one very cool deep learning concept. Essentially, they are composed of two networks that are competing against each other. The first network tries to generate images that are similar to the training set and it is called the generator. The second network tries to detect where does the image comes from, the training set, or the generator and it is called – the discriminator. Both networks are trying to be better than the other and as a result, we get better-generated images. Learn more about Autoencoders and GANs here.

The problem in the past, when it comes to building deepfakes, was that we needed some kind of additional information, ie. these techniques required some priors. For example, if we wanted to map head movement we would need needed facial landmarks. If we wanted to map full-body movement we needed to do pose-estimation.

That changed at the last year’s NeurIPS conference where the paper “First Order Motion Model for Image Animation” by the research team from the University of Toronto (Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci and Nicu Sebe) was introduced. This method doesn’t require additional information about the subject of animation. Apart from that, once this model is trained, we can use it for transfer learning and apply it to an arbitrary object of the same category.

2. First Order Motion Model for Image Animation

Let’s explore a bit how this method works. The whole process is separated into two parts: Motion Extraction and Generation. As an input the source image and driving video are used. Motion extractor utilizes autoencoder to detect keypoints and extracts first-order motion representation that consists of sparse keypoints and local affine transformations. These, along with the driving video are used to generate dense optical flow and occlusion map with the dense motion network. Then the outputs of dense motion network and the source image are used by the generator to render the target image.

This work outperforms state of the art on all the benchmarks. Apart from that it has features that other models just don’t have. The really cool thing is that it works on different categories of images, meaning you can apply it to face, body, cartoon, etc. This opens up a lot of possibilities. Another revolutionary thing with this approach is that now you can create good quality Deepfakes with a single image of the target object, just like we use YOLO for object detection.

If you want to find out more about this method, check out the paper and the code.

3. Building your own Deepfake

The code for First Order Motion Model can be downloaded from here if you have a fast GPU and want to test this app on your own environment. It contains several important folders and files. For example, within the config folder, you can find configurations for different pre-trained models. The modules folder contains an implementation of mentioned keypoint detector, discriminator, and generator. Also in the root folder, you can find demo.py, which shows how to use this repository and even train.py. The pre-trained model can be downloaded from here.

In order to use this solution, you need to download a pre-trained model. So, we are utilizing Transfer Learning to do this. For this purpose we utilize _vox-adv-cpk.pth.tar, _ which is a model for face animation.

Once you downloaded it, put it in the root of the repository. In order to use this solution, you need to download a pre-trained model. So, we are utilizing Transfer Learning to do this. Once you downloaded it, put it in the root of the repository.

To to make your deepfake video follow these steps:

  • Record driving video that you want to use and pick an image that you want to use

  • Run following command to crop the video:

  • Run this command to create deepfake video with proper parameters:

You can do so by following this Collab notebook. In essence, what you need to do is clone the repository and mount your Google Drive. Once that is done, you need to upload your image and driving video to drive. Make sure that image and video size contains only face, for the best results. Use ffmpeg to crop the video if you need to. Then all you need is to run this piece of code:

Here is my experiment with image of Nikola Tesla and a video of myself:

3. Use Cases

There are several interesting music videos done by this technique. Check them out:

Conclusion

We are living in a weird age in a weird world. It is easier to create fake videos/news than ever and distribute them. It is getting harder and harder to understand what is truth and what is not. It seems that nowadays we can not trust our own senses anymore. Even though fake video detectors are also created, it is just a matter of time before the information gap is too small and even the best fake detectors can not detect if the video is true or not. So, in the end, one piece of advice – be skeptical. Take every information that you get with a bit of suspicion because things might not be quite as it seems.

Thank you for reading!

#ai #python #artificaial inteligance #artificial neural networks #computer vision #data science

Origin Scale

Origin Scale

1616572311

Originscale Order Management System

Originscale order management software helps to manage all your orders across channels in a single place. Originscale collects orders across multiple channels in real-time - online, offline, D2C, B2B, and more. View all your orders in one single window and process them with a simple click.

#order management system #ordering management system #order management software #free order management software #purchase order management software #best order management software

Harry Patel

Harry Patel

1614145832

A Complete Process to Create an App in 2021

It’s 2021, everything is getting replaced by a technologically emerged ecosystem, and mobile apps are one of the best examples to convey this message.

Though bypassing times, the development structure of mobile app has also been changed, but if you still follow the same process to create a mobile app for your business, then you are losing a ton of opportunities by not giving top-notch mobile experience to your users, which your competitors are doing.

You are about to lose potential existing customers you have, so what’s the ideal solution to build a successful mobile app in 2021?

This article will discuss how to build a mobile app in 2021 to help out many small businesses, startups & entrepreneurs by simplifying the mobile app development process for their business.

The first thing is to EVALUATE your mobile app IDEA means how your mobile app will change your target audience’s life and why your mobile app only can be the solution to their problem.

Now you have proposed a solution to a specific audience group, now start to think about the mobile app functionalities, the features would be in it, and simple to understand user interface with impressive UI designs.

From designing to development, everything is covered at this point; now, focus on a prelaunch marketing plan to create hype for your mobile app’s targeted audience, which will help you score initial downloads.

Boom, you are about to cross a particular download to generate a specific revenue through your mobile app.

#create an app in 2021 #process to create an app in 2021 #a complete process to create an app in 2021 #complete process to create an app in 2021 #process to create an app #complete process to create an app

Fredy  Larson

Fredy Larson

1595205933

Laravel create model migration and controller in a single command

Hello guys, sometimes while working on the large application, we need to create some very small modules like simple CRUD. So here in this article i will let you know laravel create model migration and controller in a single command.

We will run a single artisan command to create model. migration and controller. We can also create resource controller by giving parameter.

We can these model, migration and controller from three command but for time saving laravel provides feature to create thses three thing by one single command.

So for desired result open your terminal and run the following command.

php artisan make:model --migration --controller Product --resource  

Here, we have create product model, migration and product controller with all the resources methods.

We can also write this command in the short form like below.

php artisan make:model -mc Product --resource

You can also write this command in a more short way like below.

php artisan make:model Product -mcr

**Note: **Here, m represents migration, c represents controller and r represents as resource.

After running the above command you will get the output like

Model created successfully

Created migration (migration file name with current date and migration name )

Controller created successfully.

You can also read the article to create to add a column in the existing table through migration by clicking on the link below.

#laravel #create controller resource and model in a command #laravel artisan command #laravel create model #laravel single command for model and migration #migration and controller

Ethen Ellen

1619519725

Immediate $olution to Fix AOL Blerk Error Code 5 with easy instructions

This is image title

AOL Email is one of the leading web email services. It has a number of features who access easily at any place. Through this, you can easily share messages, documents or files, etc.AOL Blerk Error is not a big issue. It is a temporary error and it occurs when there is an issue in loading messages from the AOL server. If your mind is stuck, How to Resolve or Fix AOL Blerk Error Code 5? Here, In this article, we mentioned troubleshooting steps to fix AOL Blerk Error Code 5.

What are the causes of AOL Blerk Error Code 5?

AOL mail usually presents an AOL Blerk Error 5 after the AOL connection details have been entered. meaning. Your password and your username. This error is usually found in words! Or 'BLERK! Error 5 Authentication problem, 'Your sign-in has been received.

Some of the reasons for the error are as follows:
• Internet browser configuration problem

• Saved erroneous bookmark addresses

• browser cache or cookie

• An AOL Desktop Gold technical error.
How to Fix AOL Mail Blerk Error 5 in a Simple Way

This type of error is mostly due to your browser settings or the use of outdated, obsolete software. Users should remember that the steps to solve problems vary, depending on the browser you are using. Here are the steps to fix the mistake, check your browser and follow the steps.

Internet Explorer: Make sure you use the most recent web browser version. Open a new window and follow the “Tools> Web Options> Security> Internet Zone” thread. Activate ‘Safeguard Mode’ and follow the steps to include AOL Mail in the list of assured websites. Start the browser again to save changes and run Internet Explorer without additional information.
Firefox Mozilla: Open a new Firefox window and press Menu. To start the browser in safe mode, disable the add-on and choose the option to restart Firefox. You can see two options in the dialog box. Use the “Start in Safe Mode” option to disable all themes and extensions. The browser also turns off the hardware speed and resets the toolbar. You should be able to execute AOL mail when this happens.

Google Chrome: Update to the latest version of Chrome. Open the browser and go to the Advanced Options section. Go to ‘Security and Privacy’ and close the appropriate add-ons. Once the browsing history is deleted, the password, cookies saved and the cache will be cleared. Restart your system and try to log in to your AOL account with a new window.

Safari: Some pop-up windows block AOL mail when it comes to Safari and causes authentication issues. To fix the error, use Safari Security Preferences to enable the pop-up window and disable the security warning.

If you see, even when you change the required browser settings, the black error will not disappear, you can consult a skilled professional and see all the AOL email customer support numbers.

Get Connect to Fix Blerk Error Even After Clearing Cache & Cookies?
Somehow you can contact AOL technical support directly and get immediate help if you still get the error. Call +1(888)857-5157 to receive assistance from the AOL technical support team.

Source: https://email-expert247.blogspot.com/2021/04/immediate-olution-to-fix-aol-blerk.html “How to Resolve or Fix AOL Blerk Error Code 5”)**? Here, In this article, we mentioned troubleshooting steps to fix AOL Blerk Error Code 5.

What are the causes of AOL Blerk Error Code 5?

AOL mail usually presents an AOL Blerk Error 5 after the AOL connection details have been entered. meaning. Your password and your username. This error is usually found in words! Or 'BLERK! Error 5 Authentication problem, 'Your sign-in has been received.

Some of the reasons for the error are as follows:
• Internet browser configuration problem

• Saved erroneous bookmark addresses

• browser cache or cookie

• An AOL Desktop Gold technical error.
How to Fix AOL Mail Blerk Error 5 in a Simple Way

This type of error is mostly due to your browser settings or the use of outdated, obsolete software. Users should remember that the steps to solve problems vary, depending on the browser you are using. Here are the steps to fix the mistake, check your browser and follow the steps.

  1. Internet Explorer: Make sure you use the most recent web browser version. Open a new window and follow the “Tools> Web Options> Security> Internet Zone” thread. Activate ‘Safeguard Mode’ and follow the steps to include AOL Mail in the list of assured websites. Start the browser again to save changes and run Internet Explorer without additional information.

  2. Firefox Mozilla: Open a new Firefox window and press Menu. To start the browser in safe mode, disable the add-on and choose the option to restart Firefox. You can see two options in the dialog box. Use the “Start in Safe Mode” option to disable all themes and extensions. The browser also turns off the hardware speed and resets the toolbar. You should be able to execute AOL mail when this happens.

  3. Google Chrome: Update to the latest version of Chrome. Open the browser and go to the Advanced Options section. Go to ‘Security and Privacy’ and close the appropriate add-ons. Once the browsing history is deleted, the password, cookies saved and the cache will be cleared. Restart your system and try to log in to your AOL account with a new window.

  4. Safari: Some pop-up windows block AOL mail when it comes to Safari and causes authentication issues. To fix the error, use Safari Security Preferences to enable the pop-up window and disable the security warning.

If you see, even when you change the required browser settings, the black error will not disappear, you can consult a skilled professional and see all the AOL email customer support numbers.

Get Connect to Fix Blerk Error Even After Clearing Cache & Cookies?

Somehow you can contact AOL technical support directly and get immediate help if you still get the error. Call +1(888)857-5157 to receive assistance from the AOL technical support team.

Source: https://email-expert247.blogspot.com/2021/04/immediate-olution-to-fix-aol-blerk.html

#aol blerk error code 5 #aol blerk error 5 #aol mail blerk error code 5 #aol mail blerk error 5 #aol error code 5 #aol error 5