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Deepfake - Do Our Eyes Deceive Us
- 1. 11/03/21 Cramma: Crystal, Ram, Emma Deepfake: Do Our Eyes Deceive Us?
- 2. AGENDA AGENDA WHAT IS DEEPFAKE/EXAMPLES FUTURE APPLICATIONS DETECTIONS BEHIND THE SCENES FUTURE CONCERNS 01 02 03 04 05 06
- 3. What is Deepfake? ● Any type of media where one person’s face is replaced by a computer-generated face ● The U.S. government has grown increasingly concerned about their potential to be used to spread disinformation and commit crimes
- 5. HISTORY OF EXAMPLES 2019 Simple to make deepfake algorithm released to public through research paper 2020 Fake political ads of Putin and Kim Jong-Un discussing state of US democracy 2018 Reddit User found posting nonconsensual fake sexual videos of individuals Sports Ads created with faces of sports icons during pandemic to advertise season MIT researchers work to create alternate historical events, Time Magazine recreates MLK march (virtual reality experience) Film on LBGTQ individuals in Russia, used deepfakes to hide identities of individuals
- 7. “Believe it when I see it” now “See it when I believe it”
- 8. BEHIND THE SCENES ● Autoencoders: ○ Using neural networks to extract face and encode into set of features and mask ○ Using another neural network to decode features, upscale face, and apply face to another image ○ Training can take weeks without GPU
- 9. BEHIND THE SCENES ● Generative Adversarial Networks (GAN): ○ Pit two neural networks against each other ○ Generator create examples that have the same statistics as original ○ Discriminator detect deviations from original data distribution ○ More time-consuming, high costs
- 10. FUTURE APPLICATIONS Message Reach Personalized voice messaging at scale Safety Reconstructing crime scenes Entertainment Digital influencers Arts Synthetic voice of same actor in different languages Innovation Trying out products digitally Education Reenacting historical figures
- 11. DETECTION ● Specialized tools are used in determination ● Detections of these videos began 3 years ago - easy tell sign years ago was that people don’t blink but quality and graphics have gotten better in recent years ● Detector tool is in early stages of development - need to prevent hacking of tools before mass release
- 12. CATEGORIES OF DETECTION ● Categories: looking at the behavior of people in the videos and differences that all deepfakes have compared to real videos ● Behavior - learn from patterns, from hand gestures to usual pauses in speech ● Differences -extract the essential data from the faces in individual frames of a video and then track them through sets of concurrent frames - inconsistencies from one frame to another
- 13. FUTURE CONCERNS ● Deepfakes make it possible to put an actor in a movie that they were never in - but also used for bad such as making explicit videos ● Deepfakes can also be used to create videos of political leaders saying things they never said ● People don’t know what is real and what is a Deepfake - leads to doubt - need a tool to ensure fake videos don’t fool the public and how real videos can be labeled as authentic ● Big companies like Facebook and Microsoft investing in technology to understand and detect Deepfakes ● Need to figure out how best to warn people about deepfakes when they are detected ● Research has shown that people remember the lie, but not the fact that it was a lie
- 14. CREDITS: This presentation template was created by Slidesgo, including icons by Flaticon, and infographics & images by Freepik. THANKS! Do you have any questions?
Editor's Notes
- After 2:02 stop
- Dr.Hany Farid, Computer Science Professor at UC Berkeley
- Deepfake technology involves two main methods that uses neural networks. The first method is called Autoencoders, which involves a pair of 3 main parts. Encoder compresses image and turns it to a vector Classification to a Convolutional Neural Network(CNN) Bottleneck (inter layer): connected/dense layer Abstract and Compressed representation of data -- make the deepfake Decoder takes vector and returns it into original image As these image datasets will be large, training can take weeks without a GPU
- This method is used more to generate synthetic faces. Embedder is the sample based on real images Generator is a convolutional neural network Goal is to artificially manufacture outputs that could easily be mistaken for real data Discriminator is a deconvolutional neural network Goal is to identify which outputs it receives have been artificially created Compares images from generator with embedder Once the discriminator can no longer tell the difference, the synthetic face has been generated
- Message Reach Personalized marketing using synthetic voice to advertise at scale Safety Reconstructing crime scenes Entertainment Korean virtual Youtuber, RUI; and various Korean & Japanese Instagram digital models (@imma.gram; @shudu.gram) Synthetically generated Arts No longer the need for dubbers, but we can synthesize an actor’s voice to speak in a different language Innovation Online fitting room where customers become the models for certain outfits Education Using voice and video to reenact historical figures to create a more immersive experience for students Emma will discuss about how to detect Deepfake content