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¿Qué es real? Cuando la IA intenta engañar al ojo humano

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Hoy en día es difícil no hablar de la Inteligencia Artificial y pensar en cómo se ha aplicado para resolver tareas difíciles y repetitivas para el ser humano. Pero en los últimos años, gracias a la llegada de las Redes Generativas Adversariales (GANs), la IA adoptó capacidades creativas que le permiten generar información artificial. Es la era de los Deepfakes, en la que puedes poner tu cara al actor de tu película favorita o ser felicitado por el presidente de los Estados Unidos. En esta charla, veremos gran parte de estas capacidades adquiridas por la IA, algunos ejemplos, y pondremos a prueba nuestro ojo para comprobar si estamos preparados para detectar que es real y que no.

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¿Qué es real? Cuando la IA intenta engañar al ojo humano

  1. 1. ¿Qué es real? Cuando la IA intenta engañar al ojo humano
  2. 2. O R G A N I Z A T I O N P L A T I N U M S P O N S O R S Thank you! C O L L A B O R A T O R S
  3. 3. What is real?
  4. 4. How do you define 'real'? If you're talking about what you can feel, what you can smell, what you can taste and see, then 'real' is simply electrical signals interpreted by your brain. MorpheusWhat about AI and its own existence?
  5. 5. A computer would deserve to be called intelligent if it could deceive a human into believing that it was human. Alan Turing
  6. 6. What is Artificial Intelligence? • More than simply code • Solve hard human tasks • Capacity to learn from the environment • Intelligent Agents Features Any system that interacts with the environment, perceives it, learn from it, and takes actions to achieve successfully its goals and tasks through flexible adaptation.
  7. 7. @ematde ematallanas@plainconcepts.com Knowmad interested in: • Changing live through AI • Passionate in robotics • Data lover • Films & series • Bike enthusiast & martial artist practioner Eduardo Matallanas AI Team Lead @ Plain Concepts
  8. 8. Things we will talk today • What is AI • Why using it • Benefits of using AI • AI applications ATTENTION: A deep change is coming!
  9. 9. Let’s look back!!
  10. 10. Artificial Intelligence Machine learning Deep learning 𝑡1950 1960 1970 1980 1990 2000 2010 Dark Ages A new hope: Alexnet
  11. 11. Why using AI now? Challenges Reducing Costs Escalating demand Optimization Usability Interactions Data Increase Increase of unstructured data Organize data Sensor information Increase articles Technology Maturity Previous effort Improved algorithms Specialized HW Cloud platforms General Access Democratization Open source code Frameworks Cognitive services Entrepreneurship Investment Specialization Innovation Business transformation
  12. 12. Applications by source •Image classification •Object detection Image •Fraud detection •Defect enhancement •Speech recognition Audio •Knowledge extraction •Information retrieval •Sentiment analysis Text •Time series analysis •Forecasting •Clustering •Dimensionality reduction Signal processing
  13. 13. AIs Victories
  14. 14. Current AI Focus • Narrow or Weak AI • Only for solving specific problems • Problems can be decomposed • Complex models based on • Model composition • Deeper structures • Towards a more general AI • Models that can solve more than one task • Making decisions • Evolve from their response and environment Which feature is necessary?
  15. 15. How about AI in fashion design?
  16. 16. D Discriminator How can we create our own fashion? G Generator Latent space Noise (s) Real Samples 𝑧(𝑥, 𝑠)𝑥 ො𝑢 = 𝐺(𝑧) 𝑦 𝐿 𝐺𝐴𝑁 𝑙𝑜𝑠𝑠 Is D correct? Fine Tune Training 𝐿1(𝑦, ො𝑢) 𝐷(𝑦, 𝑧) Generate fake samples to fool the discriminator Classify fake images vs real images
  17. 17. What about the final product?
  18. 18. Does not end here
  19. 19. What about art? Transfer style + =
  20. 20. How does it work?
  21. 21. Can I really use it in people?
  22. 22. StyleGAN = GAN + Style Transfer by NVlabs
  23. 23. Even create your own people https://generated.photos/
  24. 24. Obama wants to say something
  25. 25. Deepfake 101: Lyp syncing from audio Time-delayed LSTM ℎ0 ℎ1 ℎ2 ℎ 𝑛 𝑥0 𝑥1 𝑥2 𝑥 𝑛 𝑦0 𝑦 𝑛−2 Input video Masked Input U-net Audio Input Texturize mouth and generate it. 𝑣(𝑡)
  26. 26. Deepfake 101: • Problems • Blurring • Synchronization • Masking • Similar audio profile • Solutions • Enhance the realistic mouth • Create enhancement process for the mouth • Use better masking
  27. 27. Hablemos del mileniarismo de los Deepfakes
  28. 28. What are Deep fakes?
  29. 29. I have a dream: Ctrl + Shift + Face ≡ ⟹
  30. 30. FaceSwap: Facial Extraction Original Frame Face Detection Aligned Face
  31. 31. FaceSwap: Autoencoders ො𝑥 = ℎ 𝑊,𝑏 ≈ 𝑥
  32. 32. FaceSwap: Facial Modification Training Reconstructed A Encoder Decoder A Latent face A Latent face B Encoder Decoder B Original A Original B Reconstructed B Generation Encoder Decoder A Latent face A Latent face B Encoder Decoder B Original A Original B Reconstructed B From Face A Reconstructed A From Face B
  33. 33. FaceSwap: Facial Transfer Original Frame Reconstructed Face B from A Merged Face Color Correction Merged Frame
  34. 34. FaceSwap-GAN What about autogenerating the mask?
  35. 35. It’s showtime
  36. 36. Raúl Cimas as Mark Zuckberg Mark Zuckberg as Raúl Cimas
  37. 37. One more thing …
  38. 38. What about generating text? • OpenAI project • Based on Transformer approach • Training on larger datasets: • books, webs… • Increases: • Reading comprehension • Translation • Summarization • QA • Entire code not publish → Model with 1.5b words GPT-2
  39. 39. Conclusions • AI is key to • Automatize processes • Create new business models • It is here to help!!! • Decision making support • Towards a Generalized AI • New features → Creativity • Explaining what is inside • Harder, better, faster, stronger • Get ethics inside!!! • The risks of fakes • Images rights • Biased data • DeepFake forensics → https://github.com/ondyari/FaceForensics
  40. 40. Questions?
  41. 41. Thanks and … See you soon! Thanks also to the sponsors. Without whom this would not have been posible. O R G A N I Z A T I O N P L A T I N U M S P O N S O R S C O L L A B O R A T O R S

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