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In-Depth Interview – Sam Gregory

In-Depth Interview – Sam Gregory

Sam Gregory is Program Director of WITNESS, an organisation that works with people who use video to document human rights issues. WITNESS focuses on how people create trustworthy information that can expose abuses and address injustices. How is that connected to deepfakes?

Sam Gregory is Program Director of WITNESS. We talked to Sam about the development and challenges of new ways to create mis- and disinformation, specifically those making use of artificial intelligence. We discussed the impact of shallow- and deepfakes, and what the essential questions are with development of tools for detection of such synthetic media.

The following has been edited and condensed. 

Sam, what is your definition of a deepfake?

I use a broad definition of a deepfake. I use the phrase synthetic media to describe the whole range of ways in which you can manipulate audio or video with artificial intelligence. 

We look at threats and in our search for solutions we look at how you can change audio, how you can change faces and how you can change scenes by for example removing objects or adding objects more seamlessly. 

What is the difference to shallowfakes? 

We use the phrase shallowfake in contrast to deepfake to describe what we have seen for the past decade at scale, which is people primarily miscontextualizing videos like claiming a video is from one place when it is actually from another place. Or claiming it is from one date when it is actually from another date. Also when people do deceptive edits of videos or do things you can do in a standard editing process, like slowing down a video, we call it a shallowfake.

The impact can be exactly the same but I think it’s helpful to understand that deepfakes can create these incredibly realistic versions of things that you haven’t been able to do with shallowfakes. For example, the ability to make someone look like they’re saying something or to make someone’s face appear to do or say something that they didn’t do. Or the really seamless and much easier ability to edit within a scene. All are characteristics of what we can do with synthetic media. 

We did a series of threat modeling and solution prioritization workshops globally. In Europe, US, Brazil, Sub-Sahara Africa, South and Southeast Asia people keep on saying, we have to view both types of fakes as a continuum and we have to be looking at solutions across it. And also we need to really think about the wording we use because it may not make that much difference to an ordinary person who is receiving a WhatsApp message whether it is a shallowfake or a deepfake. It matters, whether it’s true or false.

Where do you encounter synthetic media the most at the moment?

Indisputably the greatest range of malicious synthetic media is targeting women. We know that from the research that has been done by organizations like Sensity. We have to remember that synthetic media is a category in the non-malicious, but potentially malicious usages. There is an explosion of apps that enable very simple creation of deepfakes. We are seeing deepfakes starting to emerge on those parody lines, a kind of an appropriation of images. And, at what time does software become readily available to lots of people to do moderately good deepfakes that could be used in satire, which is a positive usage but can also be used in gender-based violence?  

Where is the highest impact of deepfakes at the moment?

It is on the individual level. In terms of impact on individual women and their ability to participate in the public sphere, related to the increasing patterns of online and offline harassment that journalists and public figures face. 

Four threat areas were identified in our meetings with journalists, civic activists, movement leaders and fact-checkers that they were really concerned about in each region. 

  1. The Liars dividend, which is the idea that you can claim something is false when it is actually true which forces people to prove that it is true. This happens particularly in places where there is no strong established media. The ability to just call out everything as false benefits the powerful, not the weak. 
  2. There is no media forensics capacity amongst most journalists and certainly no advanced media forensics capacity. 
  3. Targeting of journalists and civic leaders using gender-based violence, as well as other types of accusations of corruption or drunkenness.
  4. Emphasis on threats from domestic actors. In South Africa we learned that the government is using facial recognition, harassing movement leaders or activists. 

These threats have to be kept in mind with the development of tools for detection. Are they going to be available to a community media outlet in the favelas in Rio facing a whole range of misinformation? Are they going to be available to human rights groups in Cambodia who know the government is against them? We have to understand that they cannot trust a platform like Facebook to be their ally.

Can be synthetic media used as an opportunity as well?

I come from a creative background. At WITNESS the center of our work is the democratization of video, the ability to film and edit. Clearly these are potential areas that are being explored commercially to create video without requiring so much investment.

I think if we do not have conversations about how we are going to find structured ways to respond to malicious usages, I see positive usage of these technologies being outweighed by the malicious usage. And I think there is a little bit too much of a „it will all work itself out” approach being described by many of the people in this space.

We need to look closely at what we expect of the people who develop these technologies: Are they making sure that they include a watermark? That they have a provenance tree that can show the original? Are they thinking about consent from the start?

Although I enjoy playing with apps that use these types of tools, I don’t want to deny that I think 99% of the usage of these are malicious.

We have to recognize that the malicious part of this can be highly damaging to individuals and highly disruptive to the information ecosystem.

Should we use synthetic media in satire for media literacy? 

We have been running a series of webtalks called deepfakery . One of the main questions is, what are the boundaries around satire? Satire is an incredibly powerful weapon of the weak against the powerful. So for example, in the US we see the circulation of shallowfakes and memes made on sites that say very clearly on the top that this is satire. But of course no one ever sees that original site. They just see the content retweeted by President Trump in which case it looks like it is a real claim.

So satire is playing both ways. I do think the value of satire is to help people understand the existence of this and to push them to sort of responsibly question their reaction to video.

I think the key question in the media literacy discussion is: how do we get people to pause? Not to dismiss everything but to give them the tools to question things. Give them the tools to be able to pause emotionally before they share.

From a technology point of view, what are we still missing to detect synthetic media?

Synthesis of really good synthetic media is still hard. So synthesizing a really good faceswap, or a convincing scene is still hard. What is getting easier is the ability to use apps to create something that is impactful but perhaps not believable. I think sometimes people over assume how easy it is to create a deepfake.

We’re not actually surrounded by convincing deepfakes at this point. 

A lot of our work has been thinking about detection and authentication. How do you spot evidence of media manipulation which could be detection of a deepfake or detection of a shallowfake? How to spot that a video has been miscontextualized and there is an original or an earlier version that has different edits? Then authentication, how do we trace a video over time to see it’s manipulations. 

At the moment the detection of synthetic media is, and this is the nature of the technology, an arms race between the people who will develop the detection tool and those who will use it to test and enhance their new synthesis tool. The results of detection tools are getting better but they are not at the level that you could do it at scale.

The meta question for us on detection is actually who to make this accessible to. If it is only the BBC, Deutsche Welle, France 24 and New York Times, that leaves out 90% of the world as well as ordinary people who may be targeted by this in an incredibly damaging way.

Do all journalists need to be trained in using advanced forensic technology?

One of the things we have learned as we have been working on deepfakes is that we shouldn’t exclusively focus on media forensics. I think it is important to build the media forensic skills of journalists and it is a capacity gap for almost every journalist to do any kind of media forensics with existing content. I do not think we can expect that every journalist will have that skill set. We also need to consider how we invest in e.g. regional hubs of expertise.

The bigger backdrop is that we need to build a stronger set of OSINT skills in journalism. We need to be careful not to turn this purely into a technical question around media forensics at a deep level because it is a complicated and specialist skill set.

We identified a range of areas that need to be addressed to develop tools that plug into journalistic workflows. For example that journalists are not going to rely on tools easily. They do not need just a confidence number, they need software to explain why it is coming up with this result. So, I think we need a constant interchange between journalists and researchers and tools developers and the platforms to say what the tools are that we really need as this gets more pervasive. And we need tools that potentially provide information to consumers and community leader level activists to help them do the kind of rapid debunking and rapid challenging of the kind of digital wildfire of rumors that journalists frankly often do not get too. Often community leaders are talking about things that circulate very rapidly in a Favela or a Township and journalists never get to them in a timely way. So we need to focus on journalists, but also on community leaders.

What are your three tips for consumers to deal with synthetic media? 

  1. Pause before you share the content.
  2. Consider the intention of why people are trying to encourage you to share it.
  3. To take an emotional pause when consuming media trying to understand the context of it is supported by a range of tools like the SIFT methodology or the Sheep Acronym

I don’t think it is a good idea to encourage people to think that they can spot deepfakes.

The clearest and most consistent demand we heard primarily from journalists and fact checkers is to show them if this is a mis-contextualized video so that they can then just clearly say, no this video is from 2010 and not from 2020.

Therefore reverse video search or finding similar videos is pretty important because that shallowfake problem remains the most predominant.

Many thanks Sam! Here’s the ‘Ticks or it didn’t happen‘ report that Sam mentioned. If you are interested to learn more or have questions then please get into contact with us, either via commenting on this article or via our Twitter channel.

We hope you liked it! Happy Digging and keep an eye on our website for future updates!

 Don’t forget: be active and responsible in your community – and stay healthy!

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In-Depth Interview – Sam Gregory

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Sam Gregory is Program Director of WITNESS, an organisation that works with people who use video to document human rights issues. WITNESS focuses on how people create trustworthy information that can expose abuses and address injustices. How is that connected to deepfakes?

In-Depth Interview – Jane Lytvynenko

In-Depth Interview – Jane Lytvynenko

We talked to Jane Lytvynenko, senior reporter with Buzzfeed News, focusing on online mis- and disinformation about how big the synthetic media problem actually is. Jane has three practical tips for us on how to detect deepfakes and how to handle disinformation.

From Rocket-Science to Journalism

From Rocket-Science to Journalism

In the Digger project we aim to implement scientific audio forensic functionalities in journalistic tools to detect both shallow- and deepfakes. At the Truth and Trust Online Conference 2020 we explained how we are doing this.

Today we have presented the Digger project and our approach on the Truth & Trust Online Conference. We explained all the audio forensic functionality that we are looking into and how this functionality can help us with detecting video manipulation and also with deepfake detection. The presentation is available via the conference website but we did not want to keep it from you so below you will find the full 11 minutes presentation.

If you are interested to learn more or have questions then please get into contact with us, either via commenting on this article or via our Twitter channel.

We hope you liked it! Happy Digging and keep an eye on our website for future updates!

 Don’t forget: be active and responsible in your community – and stay healthy!

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In-Depth Interview – Sam Gregory

In-Depth Interview – Sam Gregory

Sam Gregory is Program Director of WITNESS, an organisation that works with people who use video to document human rights issues. WITNESS focuses on how people create trustworthy information that can expose abuses and address injustices. How is that connected to deepfakes?

In-Depth Interview – Jane Lytvynenko

In-Depth Interview – Jane Lytvynenko

We talked to Jane Lytvynenko, senior reporter with Buzzfeed News, focusing on online mis- and disinformation about how big the synthetic media problem actually is. Jane has three practical tips for us on how to detect deepfakes and how to handle disinformation.

Video verification step by step

Video verification step by step

What should you do if you encounter a suspicious video online? Although there is no golden rule for video verification and each case may present its own particularities, the following steps are a good way to start.

What should you do if you encounter a suspicious video online? Although there is no golden rule for video verification and each case may present its own particularities, the following steps are a good way to start. 

Pay attention and ask yourself these basic questions

Start with asking some basic questions like “Could what I am seeing here be true?”, “Who is the source of the video and why am I seeing/receiving this?”. “Am I familiar with this account?”, “Has the account’s content and reporting been reliable in the past?” and “Where is the uploader based, judging by the account’s history?”. Thinking the answers to such questions may raise some red flags about why you should be skeptical towards what you see. Also, watch the video at least twice and pay close attention to the details; this remains your best shot for identifying fake videos, especially deepfakes. So, careful viewers may be able to detect certain inconsistencies in the video (e.g. non-synchronized lips or irregular background noises) or signs of editing/manipulation (e.g. certain areas of a face that are blurry or strange cuts in the video). Most video manipulation is still visible by the naked eye. If you want to read more on how to deal with dubious claims in general, you can read our previous blog post

Capture and reverse search video frames

When encountering a suspicious image, reverse searching it on Google or Yandex is one of the first steps you take in order to find out if it was used before in another context . For videos, although reverse video search tools are not commercially available yet, there are ways to work around that, in order to examine the provenance of a video and see whether similar or identical videos have circulated online in the past. There are many tools like Frame-By-Frame that enable users to view a video frame-by-frame, capture any frame and save it – if you have the VLC player installed it works as well. 

Cropping certain parts of a frame or flipping the frame (flipping images is one method disinformation actors use to make it more difficult to find the original source through reverse image search) before doing a reverse search may sometimes yield unexpected results. Also, searching in several reverse search engines (Google, Yandex, Baidu, TinEye, Karma Decay for Reddit, etc.) increases the possibility of finding the original video. The InVID-WeVerify plugin can help you verify images and videos using a set of tools like contextual clues, image forensics, reverse image search, keyframe extraction and more.

Examine the location where the video was allegedly filmed

Although in some instances it is very difficult or nearly impossible to verify the location where a video was shot, other times the existence of landmarks, reference points or other distinct signs in the video may reveal its filming location. For example, road signs, shop signs, landmarks like mountains, distinct buildings or other building structures can help you corroborate the video’s filming location.

Tools like Google Maps, Google Street View, Wikimapia, and Mapillary can be used to cross-check whether the actual filming location is the same as the alleged. Checking historical weather conditions for this particular place, date and time is another way to verify a video. Shadows visible in the video should also be cross-checked to determine whether they are consistent with the sun’s trajectory and position at that particular day and time. SunCalc is a tool that helps users check if shadows are correct by showing sun movement and sunlight phases during the given day and time at the given location. And sometimes it helps to stitch together several keyframes to narrow down the location – you may check this great tutorial by Amnesty

Video metadata and image forensics 

Even though most social media platforms remove content metadata once someone uploads a video or an image, if you have the source video, you can use your computer’s native file browser or tools like Exiftool to examine the video’s metadata. Also, with tools like Amnesty International’s YouTube DataViewer you will be able to find out the exact day and time a video was uploaded on YouTube.  If the above steps don’t yield confident results and you are still unsure of the video you can try out some more elaborate ways to assess its authenticity. With tools like the InVID-WeVerify plugin or FotoForensics you can examine an image or a video frame for manipulations with forensics algorithms like Error Level Analysis (ELA) and Double Quantization (DQ). The algorithms may reveal signs of manipulation, like editing, cropping, splicing or drawing. Nevertheless, to be able to understand the results and draw safe conclusions avoiding false-positives a level of familiarity with image forensics is required.

A critical mind and an eye for detail

As mentioned above, there is no golden rule on how to verify videos. The above steps are merely exhaustive, but they can be a good start. But as new methods of detection are developed, so are new manipulation methods – in a game that doesn’t seem to end. The commercialization of the technology behind deepfakes through openly accessible applications like Zao or Doublicat is making matters worse driving the “democratization of propaganda”. What remains most important and independent of the tools that can be used for the detection of manipulated media is to approach any kind of online information (especially user generated content) with a critical mind and an eye for detail. Traditional steps in the verification process, such as checking the source and triangulating all available information still remain central.   

In the effort to tackle mis- and disinformation, collaboration is key. In Digger we work with Truly Media to provide journalists with a working environment where they can collaboratively verify online content. Truly Media is a collaborative platform developed by Athens Technology Center and Deutsche Welle that helps teams of users collect and organise content relevant to an investigation they are carrying out and together decide on how trustworthy the information they have found is.  In order to make the verification process as easy as possible for journalists, Truly Media integrates a lot of the tools and processes mentioned above, while offering a set of image and video tools that aid users in the verification of multimedia content. Truly Media is a commercial platform – for a demo go here.

How to get started?

If you are a beginner in verification or if you would like to learn more about the whole verification process, we would suggest reading the first edition of the Verification Handbook, the Verification Handbook for Investigative Reporting, as well as the latest edition published in April 2020.

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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We talked to Jane Lytvynenko, senior reporter with Buzzfeed News, focusing on online mis- and disinformation about how big the synthetic media problem actually is. Jane has three practical tips for us on how to detect deepfakes and how to handle disinformation.

The dog that never barked

The dog that never barked

Deepfakes have the potential to seriously harm people’s lives and to deter people’s trust in democratic institutions. They also continue to make the headlines. How dangerous are they really?

Deepfakes, although characterized by some as “the dog that never barked”, have in fact the potential to seriously harm people’s lives and to deter people’s trust in democratic institutions. 

Deepfakes continue to make the headlines – the latest news at the time of writing this article being about Donald Trump’s Independence Day deepfake video – which  raised also important legal and ethical issues, almost three years after the term “deepfake” was first coined in the news. Behind the headlines, synthetically generated media content (also known as  deepfakes) have even more serious consequences on individual lives – and especially on the lives of women. Deepfakes are also expected to be increasingly weaponized and combined with other trends and technologies they are expected to heighten security and democracy challenges in areas like cyber-enabled crime, propaganda and disinformation, military deception, and international crises.

“Technical approaches are useful until synthetic media techniques inevitably adapt to them. A perfect deepfake detection system will never exist”.   Sam Gregory, program director of WITNESS

 

It´s a race

Researchers, academics, and industry are all working towards developing deepfake detection algorithms, but developments in the field occur both ways, and as new detection algorithms get better, so do available tools to create deepfakes. As Sam Gregory, program director of WITNESS puts it, “Technical approaches are useful until synthetic media techniques inevitably adapt to them. A perfect deepfake detection system will never exist”. 

Verification of synthetically generated media content is still part of the traditional verification and fact-checking techniques and should be approached in the context of these already existing methods. Even though technology cannot provide a yes-or-no answer in the question “Is this video fake?”, it can greatly aid journalists in the process of assessing the authenticity of deepfakes. That’s why we at the Digger team are working hard to provide journalists with tools that can help them determine if a certain video is real or synthetic. Stay tuned for our how-to article coming up soon!

 

Don’t forget: be active and responsible in your community – and stay healthy!

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In-Depth Interview – Sam Gregory

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Sam Gregory is Program Director of WITNESS, an organisation that works with people who use video to document human rights issues. WITNESS focuses on how people create trustworthy information that can expose abuses and address injustices. How is that connected to deepfakes?

In-Depth Interview – Jane Lytvynenko

In-Depth Interview – Jane Lytvynenko

We talked to Jane Lytvynenko, senior reporter with Buzzfeed News, focusing on online mis- and disinformation about how big the synthetic media problem actually is. Jane has three practical tips for us on how to detect deepfakes and how to handle disinformation.

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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In-Depth Interview – Sam Gregory

In-Depth Interview – Sam Gregory

Sam Gregory is Program Director of WITNESS, an organisation that works with people who use video to document human rights issues. WITNESS focuses on how people create trustworthy information that can expose abuses and address injustices. How is that connected to deepfakes?

In-Depth Interview – Jane Lytvynenko

In-Depth Interview – Jane Lytvynenko

We talked to Jane Lytvynenko, senior reporter with Buzzfeed News, focusing on online mis- and disinformation about how big the synthetic media problem actually is. Jane has three practical tips for us on how to detect deepfakes and how to handle disinformation.

Digital verification – Lessons learned from social distancing

Digital verification – Lessons learned from social distancing

Rules are in place to prevent the spread of the Coronavirus. One of them is called “social distancing” which helps to stop the transmission of Covid-19. What are the rules concerning Coronavirus information online?

Rules have been introduced across the globe about how to behave in order to prevent the spread of the Coronavirus. One of these rules is called “social distancing” which helps to stop the transmission of Covid-19. It recommends we avoid crowds, take public transport at off-peak hours and keep physical distance to other people.  

What are the rules concerning Coronavirus information online? Daily updates about the spread and protection of Covid-19 are crucial and easy to find – online – but not all of them are true. Sharing misinformation online is like refraining from social distancing; tempting, but it could harm people. So how can you contribute to stopping the transmission of dis- and misinformation about Covid-19? These are the rules on how to deal with dubious information online:

1. Ask yourself: Does this information make sense? 

Subsequent questions are: What sources does the information rely on? Where do the numbers come from?

2. Double check the information with reliable sources like; quality journalism, fact-checkers and relevant experts. Here’s how to do that:

  • Google the claim using the main keywords or the headline like in the video below:
  • Golden rule: reverse image search a photo or video
  • Use Google Dorks (a search string that uses advanced search operators) and search on a specific news sites or in a specific timeframe (see image below):
  • Check Twitter lists with reliable sources on Covid-19, like these:
    • Tutorial: Search on Tweetdeck your Twitter List
    • Here is a Twitter list curated by Journalism.Co with reliable journalists and media
    • Here is a Twitter list curated by Jeff Jarvis with medical experts
    • Here is a Twitter list curated by IFCN with national fact-checking organisations

3. Still nothing? Wait or reach out to your doctor.

Remember: Share only what is fact-checked with your family and friends in your FB, WhatsApp and other communities!

Verification of videos and synthetic media

For verification of videos there are some specific rules. Here are the most important ones:

Golden rule: reverse image search a video 

The reverse image search enables a simple and quick check whether a video has been published online before, possibly in another context. This way you might also be able to retrieve the original source of the video. You can use the InVid-Plugin for selecting several thumbnails from the video and reverse search with different tools like Google and Yandex – or you take a screenshot and do the same via Google Images.

Visual content verification

Most video manipulation is still visible with your bare eye. Look at the small details visible in the video. If you think you are watching a fake you might want to check:

  • strange cuts, non-fluent frames can indicate manipulation,
  • does the body actually fit the face of a person and does the body language match the facial expression,
  • is the person on video showing natural behaviour; eg. blinking eyes, movement of eyes, movement of hands? 

To help make it a bit easier you can check the video ‘frame by frame’ with VLC player and check if the colour and shadows are changing in a consistent way that makes sense to you.  

With a precise eye for details you could use the verification plugin with magnifier functionality from InVid/WeVerify that enhances the quality of your zoomed area in video stills.

If there are shadows visible and you are really getting into it, you can determine when on the day the video was filmed with tools like Suncalc. Here is a detailed tutorial on using shadows.

Technical video verification

The devil is in the detail, and the manipulation technology evolves. For well done synthetic video you would need some elaborate algorithms to check manipulation. We’re trying to develop those in the Digger project. What you could try is to focus on audio; check if the acoustics of a video correlate with the scene recorded – is it outside and thus with background noises, are people talking in the background, and does that match the video? And obviously if the audio does not match the lip movements with poorly implemented lip synchronization, it is more likely a fake. Still not sure, consult a forensic expert like anyone on this deepfakes forensics Twitter list.

Reliable sources

Social Media platforms like Facebook, Twitter, Youtube, Pinterest and more have taken steps against misinformation about the Coronavirus such as directing users to official sources when they search for Covid-19. 

Here are some helpful trustworthy sources for everyone of us:

  • World Health Organization – The WHO offers daily updates on the pandemic, guidance, and data on the spread. 
  • FirstDraft Resources for Reporters – Guides how to verify and a searchable archive of Coronavirus debunks.
  • Sifting Through the Pandemic – Mike Caufield’s simple and effective educational website teaches how to navigate online information. His approach runs counter to the news reader´s natural instincts. Mike has updated this post specifically for Coronavirus.
  • Fighting the Infodemic: The #CoronaVirusFacts Alliance The #CoronaVirusFacts / #DatosCoronaVirus Alliance unites more than 100 fact-checkers around the world in publishing, sharing and translating facts surrounding the Coronavirus.

Be active and responsible in your community – and stay healthy!

Related Content

In-Depth Interview – Sam Gregory

In-Depth Interview – Sam Gregory

Sam Gregory is Program Director of WITNESS, an organisation that works with people who use video to document human rights issues. WITNESS focuses on how people create trustworthy information that can expose abuses and address injustices. How is that connected to deepfakes?

In-Depth Interview – Jane Lytvynenko

In-Depth Interview – Jane Lytvynenko

We talked to Jane Lytvynenko, senior reporter with Buzzfeed News, focusing on online mis- and disinformation about how big the synthetic media problem actually is. Jane has three practical tips for us on how to detect deepfakes and how to handle disinformation.