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   1105

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”.

Nvidia to Open SPADE Source Code

SPADE machine learning system creates realistic landscapes based on rough human sketches
15 April 2019   864

NVIDIA has released the source code for the SPADE machine learning system (GauGAN), which allows for the synthesis of realistic landscapes based on rough sketches, as well as training models associated with the project. The system was demonstrated in March at the GTC 2019 conference, but the code was published only yesterday. The developments are open under the non-free license CC BY-NC-SA 4.0 (Creative Commons Attribution-NonCommercial-ShareAlike 4.0), allowing use only for non-commercial purposes. The code is written in Python using the PyTorch framework.

Sketches are drawn up in the form of a segmented map that determines the placement of exemplary objects on the scene. The nature of the generated objects is set using color labels. For example, a blue fill turns into sky, blue into water, dark green into trees, light green into grass, light brown into stones, dark brown into mountains, gray into snow, a brown line into a road, and a blue line into the river. Additionally, based on the choice of reference images, the overall style of the composition and the time of day are determined. The proposed tool for creating virtual worlds can be useful to a wide range of specialists, from architects and urban planners to game developers and landscape designers.

Objects are synthesized by a generative-adversarial neural network (GAN), which, based on a schematic segmented map, creates realistic images by borrowing parts from a model previously trained on several million photographs. In contrast to the previously developed systems of image synthesis, the proposed method is based on the use of adaptive spatial transformation followed by transformation based on machine learning. Processing a segmented map instead of semantic markup allows you to achieve an exact match of the result and control the style.

To achieve realism, two competing neural networks are used: the generator and the discriminator (Discriminator). The generator generates images based on mixing elements of real photos, and the discriminator identifies possible deviations from real images. As a result, a feedback is formed, on the basis of which the generator begins to assemble more and more qualitative samples, until the discriminator ceases to distinguish them from the real ones.