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All sorts of video manipulation

All sorts of video manipulation

What is the difference between a ‘face swap’, a ‘speedup’ or even a ‘frame reshuffling’ in a video? At the end of the day they all are manipulations of video content. We want to have a closer look into the different kinds of manipulations – whether it are audio changes, face swapping, visual tampering, or simply taking content out of context.

In Digger we look at synthetic media and how to detect manipulations in all of its forms. 

This is not a tutorial on how to manipulate video. We want to highlight the different technical sorts of manipulation and raise awareness so that you might recognise one it crosses your path. Let’s start with:

Tampering of visuals and audio

Do you remember the Varoufakis finger?! Did he show it, or didn´t he?

This clip has been manipulated by pasting in a layer of an arm of another person. It is possible to crop and add any element in a video. 

As well as deleting specific parts of audio tracks in a speech or conversation to mislead you. Be careful, also background noises can be added to change the whole context of a scene. Therefore it is important to find the original version so you can compare the videos with each other.

Synthetic audio and lip synchronisation

Imagine you can say anything fluently in 7 different languages, like David Beckham did?

It is incredible but the larger part of his video is completely synthetic. They created a 3D model of Beckham´s face and reanimate that. That means that a machine learned what David looks like, how he moves when speaking in order to reproduce David saying anything in any language. One tip by Hany Farid: Detect mouth and lip movements and compare them with your own human behaviour. This is one example for English speaking lip movements.

Cloned voices are already offered online, so make sure you search for the original version (yes, again), trusted media reports or try and get access to an official transcript if it was a public speech. 

Shallowfakes or Cheapfakes

Just by slowing down or speeding up a video the whole context can change. In this example the speed of the video has been lowered. Nancy Pelosi, US Speaker of the House and Democrats Congresswoman, seems to be drunk in an interview.

In order to correct the lower voice the pitch of the voice has been turned up. All this effort was made to make you believe that Nanci Pelosi was drunk during an interview.

In the case of Jim Acosta part of a video has been sped up in order to suggest that he is making an aggressive movement in the situation where a microphone is being taken away from him.

It shows that also non-hightech manipulations can do harm and be challenging to detect . How can you detect low-tech manipulations? Again, find the original and compare. Try playing around with the speed in your own video player for example with the VLC player. 

Face swap or Body swap 

Imagine dancing like Bruno Mars or Beyonce Knowles without any training, a dream comes true.

This highly intelligent system captures the poses and motions of Bruno Mars and maps them on the body of the amateur. Copying dance moves, arms and legs, torso and head all at once is still challenging for artificial intelligence. If you focus on the details you will be able to see the manipulation. It’s still far from perfect, but it’s possible and just a matter of time till the technology is trained better. 

Synthetic video and synthetic voice

You can change and tamper videos and audio, but what happens when you do all of it in one video? When you would be able to generate video completely synthetically? One could recreate a person who died already many years ago. Please meet Salvador Dalí anno 2019:

Hard to believe, right? Therefore, always ask yourself if what you see could be true. Check the source and search for more context on the video. Maybe a trustworthy media outlet already reported about it. If you cannot find anything, just do not share it.

The Liar´s Dividend

We also need to be prepared that people might claim that a video or audio is manipulated which actually isn’t. This is called “The Liar´s Dividend”. 

If accused of having said or done something that he/she said or did, liars may generate and spread altered sound or images to create doubt or even say the authentic footage is a deepfake.

Make sure you have your facts checked. Ask colleagues or experts for help if needed and always watch a video more than twice. 

Have you recently watched a music video? Musicians seem to be among the first professional customers for the deepfake industry. Have a look, this is where the industry is currently being built up.

Did we forget techniques for video manipulation? Let us know and we will add it to our collection in this article.

The Digger project aims:

  • to develop a video and audio verification toolkit, helping journalists and other investigators to analyse audiovisual content, in order to be able to detect video manipulations using a variety of tools and techniques.
  • to develop a community of people from different backgrounds interested in the use of video and audio forensics for the detection of deepfake content.

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Digger – Detecting Video Manipulation & Synthetic Media

Digger – Detecting Video Manipulation & Synthetic Media

What happens when we cannot trust what we see or hear anymore? First of all: don’t panic! Question the content: Could that be true? And when you are not 100 percent sure, do not share, but search for other media reports about it to double-check.

What happens when we cannot trust what we see or hear anymore? First of all: don’t panic! Question the content: Could that be true? And when you are not 100 percent sure, do not share, but search for other media reports about it to double-check.

How do professional journalists and human rights organisations do this? Every video out there could be manipulated. With video editing software anyone can edit a video.

It is challenging to verify content which has been edited, mislabeled or staged. What is even more complex is to verify content that has been modified. We roughly see two kinds of manipulation:

  1. Shallow fakes: manipulated audiovisual content (image, audio, video) generated with ‘low tech’ technologies like Cut & Paste or speed adjustments. 
  2. Deepfakes: artificial (synthetic) audiovisual content (image, audio, video) generated with technologies like Machine Learning.

Deepfakes and synthetic media are some of the most feared things in journalism today. It is a term which describes audio and video files that have been created using artificial intelligence. Synthetic media is non-realistic media and often referred to as Deepfakes at the moment. Generated by algorithms it is possible to create or swap faces, places, and digital synthetic voices that realistically mimic human speech and face impressions but actually do not exist and aren´t real. That means machine-learning technology can fabricate a video with audio to make people do and say things they never did or said. These synthetic media can be extremely realistic and convincing but are actually artificial.

Detection of synthetic media

Face or body swapping, voice cloning and modifying the speed of a video is a new form of manipulating content and the technology is becoming widely accessible

At the moment the real challenge are the so called shallow fakes. Remember the video where Nancy Pelosi appeared to be drunk during a speech. It turned out the video was just slowed down, but with the pitch turned up to cover up the manipulation. Video manipulation and creation of synthetic media is not the end of the truth but it makes us more cautious before using the content in our reporting. 

On the technology side it is a rat race. Forensic journalism can help detect altered media. DW´s Research & Cooperation team works together with ATC, a technology company from Greece and the Fraunhofer Institute for digital media technology to detect manipulation in videos. 

Digger – Audio forensics

In the Digger project we focus on using audio forensics technologies to detect manipulation. Audio is an essential part of video and with a synthetic voice of  a politician or the tampered noise of a gunshot a story can change completely. Digger aims to provide functionalities to detect audio tampering and manipulation in videos. 

Our approach makes use of:

  1. Microphone analysis: Analysing the device being used for the recording of audio. 
  2. Electrical network Frequency Analysis: Detect editing (cut & paste analyses) of audio.
  3. Codec Analysis: We follow the digital footprint of audio by extraction of ENF traces.

Synthetic media in reality

Synthetic media technologies can have a positive as well as a negative impact on society.

It is exciting and scary at the same time to think about the ability to create audio-visual content in the way we want it and not in the way it exists in reality. Voice synthesis will allow us to speak in hundreds of languages in our own voice. (Hyperlink: Video David Beckham) 

Or we could bring the master of surrealism back to life:

With the same technology you can also make politicians say something they never have or place people in scenes they have never been. These technologies are being used in pornography a lot but the unimaginable impact is also showcased in short clips in which actors are placed in films they have never acted in. Possibly one of the most harmful effects is that perpetrators can also easily claim “that’s a deepfake” in order to dismiss any contested information. 

How can the authenticity of information be proofed reliably? This is exactly what we aim to address with our project Digger.  

Stay tuned and get involved

We will publish regular updates about our technology, external developments and interview experts to learn about ethical, legal and hand-on expertise. 

The Digger project is developing a community to share knowledge and initiate collaboration in the field of synthetic media detection. Interested? Follow us on Twitter @Digger_project and send us a DM or leave a comment below. 

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In-Depth Interview – Jane Lytvynenko

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