Airports’ security is as important as national security. People, not only within the country but across the globe are going in and out of the country. The US Department of Homeland Security with the help of Google launched a contest in creating computer algorithms that will enable airports to identify hidden items in the images produced by checkpoint body scanner.
Gathering Data Scientists and Their ideas
The government aims to enhance security through advanced screening technology at airports. Kaggle which is already owned by Google and a site that holds over a million data scientists operates the said contests. The government is giving $1.5 million dollars for the contests that will run for 6 months.
Anthony Goldbloom, founder, and chief executive of Kaggle revealed that the said contest is an initiative to develop a technology named ‘deep neural networks’. These are complex mathematical systems with the ability to learn a particular task through huge data analysis. For instance, a neural network can identify what a cat is after analyzing millions of cats’ images.
Utilizing Neural Network Technology
Neural network technology is already used by Google and Facebook in several tasks. These include translation of languages, recognition of verbal commands from smartphones and identifying human faces in online images. While building algorithms were used to identify symptoms of lung cancer in CT scans in the past, neural networks are now developed to work with automated systems and read more precise body scans. This technology will not only enhance security but will also make airports services faster.
When it comes to conducting a contest, several managers and administrators find it a good idea. John W. Halinski, a security consultant considers the said contest as ‘crowdsourcing’ idea that will gather skills from different data scientists. Meanwhile, John Fortune, a program manager working in the Department of Homeland Security believes that the contest will find many people with high problem-solving skills. Moreover, Homeland Security and other agencies are in the process of discovering how neural networks can be used at security checkpoints like in the airport.
Efficiency of Neural Networks
Proponents of neural networks believe that it can upgrade airport security due to its capacity to learn data in a short time. Recently, Homeland Security supplied over a thousand three-dimensional body scans. But the scans are not shared and are not used for the contest. Volunteers from Transportation Security Administration assisted the working team in creating data by walking through a set of test scanners done in New Jersey laboratory.
Data gathered were used for analysis. The result shows that neural networks are efficient in doing security-related tasks like identifying hidden items. On the other hand, experts say that the technology is not perfect. According to some research, law offenders can change items or displaced the system to fool the system run by neural networks. In this case, the image-recognition system powered by neural networks might fail to see some concealed items.
Nevertheless, the government has seen the potential of using this technology to help human screeners in maintain top airport security. In the near future, the government and other organizations hope that neural networks will create breakthroughs in the security system and related tasks.
