New Machine Learning Algorithm to Break Captcha Easy

The GAN (generative-adversial network) based algorithm was developed by scientists from the UK and China
19 December 2018   872

An algorithm for machine learning has appeared, which bypasses the text captcha easier, faster and more precisely than previous methods: it recognizes it in 0.05 seconds using a desktop PC. The algorithm was developed by scientists from the UK and China, using the GAN - generative-adversial network.

Conventional machine learning algorithms require millions of samples of initial data for learning. Bots that capture captcha images are easy to recognize and block. The learning process itself is demanding of resources.

For the new algorithm, this amount of data is not required, which means that the attacker does not need to collect it. The neural network is undemanding to computing resources and easy to train - this reduces the cost of preparing an attack.

The researchers said that their method with 100% accuracy recognized captcha on sites such as Megaupload, Blizzard and Authorize.NET. On Amazon, PayPal, Yahoo and other resources, accuracy was less, but also high.

Researchers recommend web site owners to use alternative methods of detecting bots. For example, analyze user behavior patterns and device locations or use biometric data.

Scientists from the English Lancaster and Chinese Northwestern and Beijing universities used the Generative Adversarial Network (GAN). This class of AI algorithms is effective in scenarios where there is not a large amount of training data.

GAN is based on two competing neural networks. One generative generates samples by mixing several source ones, and the other discriminative generates attempts to decipher them. Both networks seek to win each other. In the process of joint competitive training, they significantly improve the quality of their work without the need to use a large amount of initial data.

Researchers collected a total of 500 samples from 11 captcha services used on 32 sites from the top 50 in the Alexa ranking. The developers spent only 2 hours on the collection. In the process of learning, more than 200,000 captchas were “synthesized”.

Neural Network to Create Landscapes from Sketches

Nvidia created GauGAN model that uses generative-competitive neural networks to process segmented images and create beautiful landscapes from peoples' sketches
20 March 2019   156

At the GTC 2019 conference, NVIDIA presented a demo version of the GauGAN neural network, which can turn sketchy drawings into photorealistic images.

The GauGAN model, named after the famous artist Paul Gauguin, uses generative-competitive neural networks to process segmented images. The generator creates an image and transfers it to the discriminator trained in real photographs. He in turn pixel-by-pixel tells the generator what to fix and where.

Simply put, the principle of the neural network is similar to the coloring of the coloring, but instead of children's drawings, it produces beautiful landscapes. Its creators emphasize that it does not just glue pieces of images, but generates unique ones, like a real artist.

Among other things, the neural network is able to imitate the styles of various artists and change the times of the day and year in the image. It also generates realistic reflections on water surfaces, such as ponds and rivers.

So far, GauGAN is configured to work with landscapes, but the neural network architecture allows us to train it to create urban images as well. The source text of the report in PDF is available here.

GauGAN can be useful to both architects and city planners, and landscape designers with game developers. An AI that understands what the real world looks like will simplify the implementation of their ideas and help you quickly change them. Soon the neural network will be available on the AI ​​Playground.