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1. But in order to learn how to spot deepfakes, you first have to make them.
2. That takes place at the University of Colorado in Denver, where researchers working on DARPA’s program are trying to create convincing deepfake videos.
3. The Hader video is an expertly crafted deepfake, a technology invented in 2014 by Ian Goodfellow, a Ph.D. student who now works at Apple.
4. “At the most basic level, deepfakes are lies disguised to look like truth,” says Andrea Hickerson, Director of the School of Journalism and Mass Communications at the University of South Carolina.
5. A lot of the fear about deepfakes rightfully concerns politics, Hickerson says.
6. “What happens if a deepfake video portrays a political leader inciting violence or panic?
7. So far, deepfakes have been limited to amateur hobbyists putting celebrities' faces on porn stars' bodies and making politicians say funny things.
8. Political hyperbole skewed by frustrated ambition, or are deepfakes really a bigger threat than nuclear weapons?
9. Deepfakes exploit this human tendency using generative adversarial networks (GANs), in which two machine learning (ML) models duke it out.
10. The larger the set of training data, the easier it is for the forger to create a believable deepfake.
11. There are even examples of deepfakes on YouTube that better the original CGI footage used in films, like the re-remastered Princess Leia featured on this list.
12. Deepfakes now appear in many movies (for example, in some of our favourite 3D movies), and they are becoming more part of the mainstream process.
13. Here, we look at 10 of the most famous deepfakes.
14. Deepfakes are so-named because they use deep learning technology, a branch of machine learning that applies neural net simulation to massive data sets, to create a fake.
15. and you’ve seen a deepfake.
16. The 21st century’s answer to Photoshopping, deepfakes use a form of artificial intelligence called deep learning to make images of fake events, hence the name deepfake.
17. The AI firm Deeptrace found 15,000 deepfake videos online in September 2019, a near doubling over nine months.
18. As new techniques allow unskilled people to make deepfakes with a handful of photos, fake videos are likely to spread beyond the celebrity world to fuel revenge porn.
19. High-quality deepfakes are often achieved by training on hours of footage of the target.
20. In videos containing deepfakes, artifacts such as flickering and jitter can occur because the network has no context of the preceding frames.
21. A mobile deepfake app, Impressions, was launched in March 2020.
22. Deepfakes technology can not only be used to fabricate messages and actions of others, but it can also be used to revive individuals who have passed away.
23. We already know DeepFakes can be quite believable, but just how believable are they?
24. Kaggle's Deepfake Detection Challenge (DFDC) recently sought an algorithmic answer to this question of detecting fakes.
25. The goal of the challenge is to spur researchers around the world to build innovative new technologies that can help detect deepfakes and manipulated media.
26. From 100,000 DeepFake videos and 19,154 real videos hosted on the public Kaggle competition, we trained a series of neural networks to detect DeepFakes.
27. AI generated fake videos first caught the public's attention in late 2017, when a Reddit account with the name Deepfakes posted pornographic videos generated with a DNN-based face-swapping algorithm.
28. While there are interesting and creative applications of deepfakes, they are also likely to be weaponized.
29. These provide us effective tools to expose deepfakes that are automated and mass-produced by AI algorithms.
30. However, whether we are going to see any form of deepfake videos in the upcoming elections will be largely determined by non-technical considerations.
31. The abilities to detect and analyze deepfake videos is of the utmost urgency.
32. The election’s on: Now Canadians should watch out for dumbfakes and deepfakes Fake videos pose a risk to democratic representation, participation, and discussion.
33. Deepfake videos could destroy trust in society – here’s how to restore it More democratic forms of politics, journalism and fact-checking will be needed when we can no longer trust any video footage.
34. Advances in deepfake technology have made it harder to distinguish between real and fabricated media, posing a very real threat to organizations.
35. Deepfake—a combination of the words ‘deep learning’ and ‘fake’—refers to an AI-based technology used to create or alter images, audio, and video resulting in synthetic content that appears authentic.
36. With the world more connected by digital media and the costs for creating deepfakes slumping dramatically, this emerging technology poses a serious risk to your organization.
37. You’re probably already familiar with a simpler version of deepfakes used by social media companies like TikTok and Snapchat.
38. Deep fake (also spelled deepfake) is a type of artificial intelligence used to create convincing images, audio and video hoaxes.
39. An example use case includes when a health charity in the UK used a deepfake to have David Beckham deliver an anti-malaria message.
40. Deepfake content is created by using two competing AI algorithms -- one is called the generator and the other is called the discriminator.
41. Because deepfakes are created through AI, however, they don't require the considerable skill that it would take to create a realistic video otherwise.
42. I knew this deepfake would be coming.
43. We're deepfaking the deepfakes now.
44. This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations.
45. The term deepfake melds two words: deep and fake.
46. Deepfakes are artificial images and sounds put together with machine-learning algorithms.
47. But deepfake technology goes a lot further in how it manipulates visual and audio content.
48. A deepfake seeks to deceive viewers with manipulated, fake content.
49. Furthermore, the latest trend of using Artificial Intelligence (AI) to create fake videos, known as "DeepFakes" or "FakeNews 2.0", is a fast-growing phenomenon creating major concerns.
50. Although DeepFakes are not as prevalent and widespread as Fake News articles, they are increasing in popularity and have a much greater effect on the general population.
51. As the usage and threat of DeepFakes and Fake News intensifies, so do efforts to develop new detection methods, resulting in the next wave of (mis)information warfare.
52. Quality of Video Analysis - analyzing the differences between deepfakes and real videos.
53. Indeed, the first widespread use of synthetic media – non-consensual deepfake pornography, which almost exclusively targets women – has proliferated wildly since it first emerged at the end of 2017.
54. In 2021, deepfakes will develop further as weapons of fraud and political propaganda, and we are already seeing examples of this.
55. A world in which deepfakes flourish will also be one in which doubt can be cast on documented evidence of wrongdoing.
56. In 2021 we must begin to fight back against deepfakes by defining the problem as the corrosion of the entire information ecosystem itself.
57. The technology used to create such digital content has quickly become accessible to the masses, and they are called "deepfakes.
58. "Deepfakes are raising a set of challenging policy, technology, and legal issues.
59. The word deepfake combines the terms "deep learning" and "fake," and is a form of artificial intelligence.
60. In simplistic terms, deepfakes are falsified videos made by means of deep learning, said Paul Barrett, adjunct professor of law at New York University.
61. Deepfake videos in themselves evoke a range of emotions — a light-hearted chuckle, disgust, and even downright horror– depending on what purpose it has been created for.
62. With the advent of easy deepfake video-making apps, even a novice has got access to creating them.
63. India saw its first use of deepfake for election purposes in 2020.
64. While this particular use of deepfakes was not as outrageous, relatively, it was quickly pointed out that it sets a precedent for future elections and other parties may follow suit.
65. No law regulates deepfakes , though some legal and technical experts have recommended adapting current laws covering libel, defamation, identity fraud or impersonating a government official.
66. But concerns of overregulation abound: The dividing line between a parody protected by the First Amendment and deepfake political propaganda may not always be clear-cut.
67. That data can then be processed in order to create a Deepfake video through a GAN (Generative Adversarial Network).
68. This makes Deepfake an ever more potent threat.
69. Deepfake examples are not hard to find.
70. One example of a Deepfake is the video issued by actor Jordan Peele in which he used real footage of Barack Obama merged with his own impression of Obama to issue a warning against Deepfake videos.
71. One major issue is deepfakes, or synthetic media, which are photos, videos or audio files manipulated by artificial intelligence (AI) in hard-to-detect ways.
72. As all AI detection methods have rates of failure, we have to understand and be ready to respond to deepfakes that slip through detection methods.
73. No single organization is going to be able to have meaningful impact on combating disinformation and harmful deepfakes.
74. Video Authenticator will initially be available only through RD2020, which will guide organizations through the limitations and ethical considerations inherent in any deepfake detection technology.
75. Some so-called “deepfakes” are harmless fun, but others are made with a more sinister purpose.
76. “The big difference is that we scan real people and use it, while deepfakes take data from other people and use it.
77. You understand how these deepfakes are being done.
78. Future research will seek to improve and refine the FakeCatcher technology, drilling further down into the data to determine how the deepfakes are made.
79. Social-media companies are concerned that deepfakes could soon flood their sites.
80. “Deepfakes are currently not a big issue,” says Facebook’s CTO, Mike Schroepfer.
81. Facebook has also announced the winner of its Deepfake Detection Challenge, in which 2,114 participants submitted around 35,000 models trained on its data set.
82. “Even very high-quality deepfakes have some flickering between frames,” says Seferbekov.
83. Amini’s Obama video was, in fact, a deepfake—an AI-doctored video in which the facial movements of an actor are transferred to that of a target.
84. Since first appearing in 2018, deepfake technology has evolved from hobbyist experimentation to an effective and dangerous tool.
85. Deepfake applications work in various ways.
86. Deepfake technology isn't the only kind that can swap faces in videos.
87. Facebook wants to be able to automatically and accurately detect deepfakes at scale.
88. Deepfakes intended to spread misinformation are already a threat to online discourse, and there is every reason to believe this problem will become more significant in the future.
89. So far, most ongoing research and mitigation efforts have focused on automated deepfake detection, which will aid deepfake discovery for the next few years.
90. In addition to supporting the near-term creation and responsible dissemination of deepfake detection technology, policymakers should invest in discovering and developing longer-term solutions.
91. Deepfakes are audio, images, and videos that appear to realistically depict speech and actions, but are actually synthetic representations made using modern artificial intelligence.
92. Deepfakes have inspired dread since the term was first coined three years ago.
93. The most widely discussed scenario is a deepfake smear of a candidate on the eve of an election.
94. Will deepfakes erode truth and trust across the financial system, requiring a major response by the financial industry and government?
95. No form of digital disinformation has managed to create a true financial meltdown, and deepfakes are unlikely to be the first.
96. This report is the second in a three-part series exploring deepfakes as an emerging disinformation threat.
97. In the first paper, we provided an overview of the deepfake threat.
98. Some of these actors are pictured here (top) with an example deepfake (bottom), which can be a subtle or drastic change, depending on the other actor used to create them.
99. So far, a large number of deepfake videos (known as "deepfakes") have been crafted and uploaded to the internet, calling for effective countermeasures.
100. Detectors developed on these datasets may become less effective against real-world deepfakes on the internet.
101. WildDeepfake is a small dataset that can be used, in addition to existing datasets, to develop and test the effectiveness of deepfake detectors against real-world deepfakes.
102. Attention-based Deepfake Detection Networks (ADDNets) to leverage the attention masks on real/fake faces for improved detection.
103. The weaponization of deepfakes can have a massive impact on the economy and national security, it can inflict harm to individuals and democracy.
104. Deepfakes can contribute to factual relativism and enables authoritarian leaders to thrive.

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