A joint academic research project from the United States has developed a method to foil CAPTCHA* tests, reportedly outperforming similar state-of-the-art machine learning solutions by using Generative Adversarial Networks (GANs) to decode the visually complex challenges. Testing the new system against the best current frameworks, the researchers found that their method achieves more than 94.4% success on a carefully curated real-world benchmark dataset, and has proved capable of'eliminating human involvement' when navigating a highly CAPTCHA-protected emerging Dark Net Marketplace, automatically resolving CAPTCHA challenges in a maximum of three attempts. The authors contend that their approach represents a breakthrough for cybersecurity researchers, who traditionally have had to bear the costs of supplying humans-in-the-loop to manually solve CAPTCHAs, usually via crowdsourcing platforms such as Amazon Mechanical Turk (AMT). If the system can prove adaptable and resilient, it may further pave the way for more automated oversight systems, and for the indexing and web-scraping of TOR networks. This could enable scalable and high-volume analyses, as well as the development of new cybersecurity approaches and techniques, which have been hamstrung, to date, by CAPTCHA firewalls.

Captcha is computer generating text images used to distinguish interactions given by humans or machines. Normally, a captcha image consists of a fixed number of characters (e.g. These characters are not only distorted, scaled into multiple different sizes but also can be overlapped and crossed by multiple random lines. Two types of captcha illustrated in Figure 1, are specified by the number of character categories (0..9, A..Z) and the text length (e.g. 5 in green and 6 in black images). In this blog, I present and compare two deep learning models solving this captcha recognition challenge.

CAPTCHA is a human-centred test to distinguish a human operator from bots, attacking programs, or other computerised agents that tries to imitate human intelligence. In this research, we investigate a way to crack visual CAPTCHA tests by an automated deep learning based solution. The goal of this research is to investigate the weaknesses and vulnerabilities of the CAPTCHA generator systems; hence, developing more robust CAPTCHAs, without taking the risks of manual try and fail efforts. We develop a Convolutional Neural Network called Deep-CAPTCHA to achieve this goal. The proposed platform is able to investigate both numerical and alphanumerical CAPTCHAs. To train and develop an efficient model, we have generated a dataset of 500,000 CAPTCHAs to train our model. In this paper, we present our customised deep neural network model, we review the research gaps, the existing challenges, and the solutions to cope with the issues. Our network's cracking accuracy leads to a high rate of 98.94% and 98.31% for the numerical and the alpha-numerical test datasets, respectively. That means more works is required to develop robust CAPTCHAs, to be non-crackable against automated artificial agents. As the outcome of this research, we identify some efficient techniques to improve the security of the CAPTCHAs, based on the performance analysis conducted on the Deep-CAPTCHA model.

In today's online market place, visual marketing is an integral part of reaching out to customers. Research show that the human brain is more visual. Up to 80% of people remember what they see compared to only 20% of what they read. This is one of the main reasons why visual content resonates more than written content. It has a greater chance of going viral.

CAPTCHAs were designed to prevent computers from automatically filling out forms by verifying that you are a real person. But with the rise of deep learning and computer vision, they can now often be defeated easily. I've been reading the excellent book Deep Learning for Computer Vision with Python by Adrian Rosebrock. Adrian didn't have access to the source code of the application generating the CAPTCHA image. To break the system, he had to download hundreds of example images and manually solve them to train his system.