Tag Archive for: vision

Is computer vision the cure for school shootings? Likely not • The Register

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Comment More than 250 mass shootings have occurred in the US so far this year, and AI advocates think they have the solution. Not gun control, but better tech, unsurprisingly.

Machine-learning biz Kogniz announced on Tuesday it was adding a ready-to-deploy gun detection model to its computer-vision platform. The system, we’re told, can detect guns seen by security cameras and send notifications to those at risk, notifying police, locking down buildings, and performing other security tasks. 

In addition to spotting firearms, Kogniz uses its other computer-vision modules to notice unusual behavior, such as children sprinting down hallways or someone climbing in through a window, which could indicate an active shooter.

If you’re wondering about the code’s false positive or error rate, Kogniz says it has “a trained team of human verifiers” checking the results of its detection software. Either you welcome that extra level of confirmation, or see it as AI potentially falling back on humans right when the computers are needed most.

“[Our solution is] making it dramatically easier for companies, governmental agencies, schools, and hospitals to prepare for and then help reduce the harm done by an active shooter event,” said Kogniz CEO Daniel Putterman.

Kogniz is not the first computer-vision company to get into the gun recognition game – there is a considerable list of companies deploying similar technology and some, such as ZeroEyes, specialize in nothing but gun detection. 

“By spreading their attention across multiple offerings, developers are less able to provide the very best service in gun detection,” ZeroEyes said in a blog post. ZeroEyes’ technology has been deployed at schools in 14 states, including Oxford High School in metro Detroit, where a 15-year-old shooter killed four and injured seven last year.

Other vendors – such…

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Computer vision can help spot cyber threats with startling accuracy

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This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence.

The last decade’s growing interest in deep learning was triggered by the proven capacity of neural networks in computer vision tasks. If you train a neural network with enough labeled photos of cats and dogs, it will be able to find recurring patterns in each category and classify unseen images with decent accuracy.

What else can you do with an image classifier?

In 2019, a group of cybersecurity researchers wondered if they could treat security threat detection as an image classification problem. Their intuition proved to be well-placed, and they were able to create a machine learning model that could detect malware based on images created from the content of application files. A year later, the same technique was used to develop a machine learning system that detects phishing websites.

The combination of binary visualization and machine learning is a powerful technique that can provide new solutions to old problems. It is showing promise in cybersecurity, but it could also be applied to other domains.

Detecting malware with deep learning

The traditional way to detect malware is to search files for known signatures of malicious payloads. Malware detectors maintain a database of virus definitions which include opcode sequences or code snippets, and they search new files for the presence of these signatures. Unfortunately, malware developers can easily circumvent such detection methods using different techniques such as obfuscating their code or using polymorphism techniques to mutate their code at runtime.

Dynamic analysis tools try to detect malicious behavior during runtime, but they are slow and require the setup of a sandbox environment to test suspicious programs.

In recent years, researchers have also tried a range of machine learning techniques to detect malware. These ML models have managed to make progress on some of the challenges of malware detection, including code obfuscation. But they present new challenges, including the need to learn too many features and a virtual environment to analyze the target samples.

Binary visualization can…

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Gee Rittenhouse And Cisco’s Vision To Democratize Security

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When one thinks of Cisco, it is typically in regard to networking infrastructure and the associated products, software and services. However, I wanted to learn more about the company’s focus on security, an area where I have recently expanded my coverage as an analyst. The other day I had the opportunity to speak with Gee Rittenhouse, who leads Cisco’s Security Business Group as General Manager and Senior Vice President. During our one-on-one, we discussed several topics, including his overall vision for security, three critical priorities for Cisco’s SecureX portfolio and Cisco’s contributions to the global cybersecurity community.

Deep experience forged in the cloud and security  

Before jumping into our conversation, it is helpful to know Mr. Rittenhouse’s background. In total, he has been at Cisco for over a decade, heading up the security division for the last five years, and the Cloud and Virtualization Group for the seven years prior to that. This is an impressive pedigree for his position, given how cloudified and virtualized in nature connectivity is today. Furthermore, these days it is delivered increasingly as a service with integrated security, such as Secure Access Service Edge (SASE). Before Cisco, Mr. Rittenhouse served as president of Bell Labs that capped a nearly fifteen-year total tenure with Alcatel-Lucent. As far as education goes, he holds a Ph.D. in electrical engineering and computer science from the esteemed Massachusetts Institute of Technology.  

A vision rooted in three key priorities

During our conversation, Mr. Rittenhouse shared his overall vision for Cisco’s security portfolio. At the heart of it all, Cisco’s goal is to reduce the complexity of deploying and managing security within the enterprise. This is no easy task. There is considerable research that supports the fact that the average midsized to large company often manages more than thirty or more security endpoint solutions. These offerings come in the form of firewalls, antivirus, virtual private networking (VPN), web filtering, threat hunting, active defense and many others.

Cisco aims to “democratize security” making it…

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How MIT Researchers Are Commercializing RFID, Computer Vision Robotics

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The MIT Media Lab system employs RFID technology to enable a robot to find a specific item in a complex environment and take instructions.

CAMBRIDGE, Mass. — Researchers at the MIT Media Lab are employing radio frequency identification (RFID) technology along with computer vision to enable robots to explore their environment in order to locate and move a targeted item that may not be visible. The system, which has been in development, simulation and testing for several years, employs machine learning to better accomplish such complex tasks, and the team is seeking to commercialize the research.

In that effort, the researchers have been interviewing potential customers and planning a possible company spinoff. This year, the team has participated in the I-Corps program, led by the  National Science Foundation to identify potential sponsors and plan the first product. “The technology has matured enough to take it out of the lab into the real-world environment,” says Fadel Adib, an MIT associate professor and the Media Lab‘s principal investigator.

The RFID portion of the robotic system employs what researchers call RF perception, consisting of off-the-shelf passive UHF RFID tags, as well as an RFID reader and specialized antennas installed in the robot’s environment. Robots employ RFID to identify items and their specific locations when they are not visible, and the software analyzing that data can direct the robots via computer vision to focus on the items before them, determine what needs to be moved or navigated around, and act accordingly. The technology, the researchers say, could be leveraged by manufacturers, retailers or warehouses to sort, pick or place goods.

The robot is designed for two primary solutions, according to Adib. One is monitoring goods moving through warehouses that need to be picked and packed according to customer orders, which traditionally requires workers to move through aisles, opening boxes and finding specific items, then placing them in containers for shipping. With RFID, the robots could identify what is in a given box or on a particular shelf, then pick up that item and confirm where it was placed. The system is designed to prevent…

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