Google has banned the training of AI systems that can be used to generate deepfakes on its Google Colaboratory platform. The updated terms of use, spotted over the weekend by Unite.ai and BleepingComputer, includes deepfakes-related work in the list of disallowed projects. Colaboratory, or Colab for short, spun out from an internal Google Research project in late 2017. It's designed to allow anyone to write and execute arbitrary Python code through a web browser, particularly code for machine learning, education and data analysis. For the purpose, Google provides both free and paying Colab users access to hardware including GPUs and Google's custom-designed, AI-accelerating tensor processing units (TPUs).

Google has banned the training of AI systems that can be used to generate deepfakes on its Google Colaboratory platform. The updated terms of use, spotted over the weekend by BleepingComputer, includes deepfakes-related work in the list of disallowed projects. Colaboratory, or Colab for short, spun out from an internal Google Research project in late 2017. It's designed to allow anyone to write and execute arbitrary Python code through a web browser, particularly code for machine learning, education and data analysis. For the purpose, Google provides both free and paying Colab users access to hardware including GPUs and Google's custom-designed, AI-accelerating tensor processing units (TPUs).

A collaboration between a Chinese AI research group and US-based researchers has developed what may be the first real innovation in deepfakes technology since the phenomenon emerged four years ago. The new method can perform faceswaps that outperform all other existing frameworks on standard perceptual tests, without needing to exhaustively gather and curate large dedicated datasets and train them for up to a week for just a single identity. For the examples presented in the new paper, models were trained on the entirety of two popular celebrity datasets, on one NVIDIA Tesla P40 GPU for about three days. In this sample from a video in supplementary materials provided by one of the authors of the new paper, Scarlett Johansson's face is transferred onto the source video. CihaNet removes the problem of edge-masking when performing a swap, by forming and enacting deeper relationships between the source and target identities, meaning an end to'obvious borders' and other superimposition glitches that occur in traditional deepfake approaches.

Deepfake defense not only requires the research of detection but also requires the efforts of generation methods. However, current deepfake methods suffer the effects of obscure workflow and poor performance. To solve this problem, we present DeepFaceLab, the current dominant deepfake framework for face-swapping. It provides the necessary tools as well as an easy-to-use way to conduct high-quality face-swapping. It also offers a flexible and loose coupling structure for people who need to strengthen their pipeline with other features without writing complicated boilerplate code. We detail the principles that drive the implementation of DeepFaceLab and introduce its pipeline, through which every aspect of the pipeline can be modified painlessly by users to achieve their customization purpose. It is noteworthy that DeepFaceLab could achieve cinema-quality results with high fidelity. We demonstrate the advantage of our system by comparing our approach with other face-swapping methods.For more information, please visit:https://github.com/iperov/DeepFaceLab/.

What are deepfakes? Deepfake technology is an evolving form of artificial intelligence that’s adept at making you believe certain media is real, when in fact it’s a compilation of doctored images and audio designed to fool you. A surge in what’s known as “fake news” shows how deepfake videos can trick audiences into believing made-up stories. What is a deepfake? The term deepfake melds two words: deep and fake. It combines the concept of machine or deep learning with something that isn’t real. Deepfakes are artificial images and sounds put together with machine-learning algorithms. A deepfake creator uses deepfake technology to manipulate media and replace a real person’s

While deepfakes have now been around for a number of years, deepfake casting and acting are relatively new. Early deepfake technologies weren't very good, used primarily in dark corners of the internet to swap celebrities into porn videos without their consent. But as deepfakes have grown increasingly realistic, more and more artists and filmmakers have begun using them in broadcast-quality productions and TV ads. This means hiring real actors for one aspect of the performance or another. Some jobs require an actor to provide "base" footage; others need a voice.

Over a few years, applications of the Generative Adversarial Networks (GANs) have seen astounding growth. The technique has been successfully used for high-fidelity natural image synthesis, data augmentation tasks, improving image compressions, and more. From emoting super-realistic expressions to exploring deep space, and from bridging the human-machine empathetic disconnect to introducing new art forms, GANs have it all covered. Here, we list down a few impressive real-world applications of GANs. Who imagined that one day we will be able to see the expressions of the Italian noblewoman Lisa Gherardini from the famous portrait of Mona Lisa painted by the Italian artist Leonardo da Vinci.