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Facial Recognition Software
Published: February 21, 2018
Source: mass private I
Everywhere you turn politicians and corporations are trying to convince the public we need to convert our cities into ‘smart cities’.
Last week AnyVision and Nvidia announced that they are working together to put facial recognition cameras in cities across the globe.
AnyVision is an Israel-based company that profits from spying on everyone.
Five months ago, I warned everyone that Nvidia also wants to turn police vehicles into 360 degree facial recognition platforms.
Facial recognition cameras are being used to spy on everyone.
Facial recognition cameras identify marathon runners in real-time
AnyVision claims their facial recognition technology can detect, track and recognize any person of interest with more than 99% accuracy. Their video also claims they can identify marathon runners in real-time.
Soon nowhere will be safe from law enforcement’s prying eyes.
“AnyVision utilizes Nvidia hardware to achieve high-speed, real-time face recognition from surveillance video streams. Our system is highly optimized for GPU acceleration allowing us to deliver real-time analysis of streaming data whilst achieving unprecedented accuracy.”
Mashable asks “is this technology terrifying, and possibly everything Orwell warned us about? Absolutely”.
Nearly a year ago, I warned everyone that ‘smart cities’ are being run by the CIA, DHS and the NDOT.
But it gets worse; law enforcement is using private companies to do an end-run around our Bill of Rights.
Companies like Phillips Lighting, Siemens, Intellistreets, ShotPoint and ShotSpotter are all working together to install facial recognition cameras and microphones in every city in America.
Don’t be fooled, ‘smart cities’ are really just a euphemism for total control.
They’re only in use in China…for now.
February 21, 2018
An Orwellian new tech gadget is helping China expand its already massive surveillance state, and it may only be a matter of time until other countries take an interest in the device. Police in central China are the early adopters of sunglasses outfitted with face-recognition technology that can pick a suspect out of a crowd. The Wall Street Journal reports that Beijing manufacturer LLVision Technology Corp. has said in early tests, “the device has been able to identify individuals in a database of 10,000 suspects in as little as 100 milliseconds.”
That means in the very near future, it may be nearly impossible to get lost in a crowd.
Here’s how it works: Wearers of the smart sunglasses scan a large group of people while the glasses collect biometric information from the faces in the group. Cameras mounted on the glasses run captured images through an offline database of faces to determine a perfect match. For years, Chinese officials have been collecting biometric information including eye scans, blood types and even “voice pattern” samples from citizens in various provinces. Human Rights Watch reported last year that China’s law enforcement databases “have more than one billion faces and 40 million people’s DNA samples.” With such a vast collection of data, results from pilot runs of the glasses have already yielded results.
Transit cops in Zhengzhou, home to one of China’s biggest and busiest train stations, have worn the glasses while they monitor the millions of commuters traveling for Lunar New Year, the largest annual migration on Earth. A state-run newspaper claims the glasses have helped cops bust “seven people wanted in connection with major criminal cases, and 26 others who were traveling using other people’s identities.”
There are already more than 170 million surveillance cameras across China, and the government has announced 400 more will be installed in the next three years. But while CCTV cameras are highly effective tools for ferreting out suspects (and spying on citizens), they don’t offer the speediness of the new camera devices. “In many cases, by the time authorities rush to where a suspect has been identified, their target has melted back into the crowd,” WSJ notes. That problem is erased by these all-seeing, artificial intelligence sunglasses, which allow wearers to keep subjects locked in their sights.
“By making wearable glasses, with AI on the front end, you get instant and accurate feedback,” Wu Fei, CEO of LLVision, told WSJ. “You can decide right away what the next interaction is going to be.”
Without crossing the line from healthy concern to paranoia, it’s worth wondering if this new surveillance advancement could end up being used to keep a watchful eye on American citizens. There are already more than 35 million surveillance cameras across the U.S., and the use of facial recognition technology has been steadily expanding. In 2016, a study by the Georgetown Law Center on Privacy and Technology found that roughly half of Americans have their pictures in law enforcement facial recognition networks. According to an ACLU report, “the Baltimore Police Department used [facial recognition] to locate, identify and arrest certain people protesting Freddie Gray’s death in police custody” and “the Los Angeles Police Department deployed to undisclosed locations 16 wireless video cameras that can conduct real-time face recognition.” Another ACLU cautionary report on the U.S. Customs and Border Protection’s Traveler Verification Service warns of the program’s mission to use facial recognition technology on every passenger boarding a flight bound for outside of America’s borders. Raising the concern of mission creep, the ACLU points out that facial recognition technology has “higher error rates” when assessing the faces of African Americans and women and children of all races.
A less advanced version of the glasses, lacking facial recognition technology, has reportedly been shipped to parts of Africa, Europe, Japan and the U.S. But WSJ indicates that LLVision, like every money-making entity, wants to increase sales of its newest spy gadget far beyond the borders of its home country. That could very well lead to bulk sales of its new glasses to law enforcement entities in other countries.
“There might be an opportunity there,” Wu suggested to the outlet. “Who knows?”
by Zoey Sky
January 15, 2018
Do we really need an app that makes it easier for complete strangers to identify us and access our social media profiles?
Blippar, a firm based in London, has recently released an augmented reality (AR) app that instantly “scans faces and brings up a profile with information about the person including links to their social media profiles.”
The AR app, called Augmented Reality Face Profile, recognizes more than 400,000 public figures. Blippar claims that the app has a “more than 99 percent accuracy rate.” The technology is advanced enough to distinguish celebrities such as the Olsen twins (actresses), the Brownlee Brothers (British Olympic runners), and Jedward (Irish pop duo).
Augmented Reality Face Profile mostly identifies public figures such as “actors, politicians, musicians, singers, entrepreneurs, authors, sports stars, and scientists. Blippar adds that the profiles are a “strictly opt-in experience.”
The app merges “rapid computer vision and artificial intelligence” to recognize faces. The system might even be used for security checks for building access and even online banking.
Danny Lopez, the company’s chief operating officer, says that the facial recognition feature of smartphones helps satisfy natural human curiosity. He added, “The technology’s accuracy in distinguishing even identical twins is the solution of a complex challenge for the industry, and the wider applications are diverse and game-changing, from building access to smart networking, security, and fraud prevention.” (Related: Shady Secret app that let people post anonymous social media bullying messages now shut down; so why is Wikipedia still online?)
The Blippar app is available for both iOS and Android.
How does Augmented Reality Face Profile work?
The Augmented Reality Face Profile lets users scan faces to reveal more information about them. App users can “add connections to things that already exist in Blippar’s ‘knowledge graph,’” such as “objects, concepts, and entities.” When scanning the faces of public figures, their faces are “discoverable” using data from the knowledge graph, which collates information from “publicly accessible sources.”
Ambarish Mitra, co-founder and CEO at Blippar, explains that Augmented Reality Face Profiles can revolutionize how “we communicate and express ourselves.” Mitra adds that although the human face is the “most expressive form of communication,” the app digitizes the whole process.
Combined with facial recognition technology and the knowledge graph, Blippar shares that the app can let individuals express themselves through their various interests such as “their hobbies, opinions, key fun facts,” and other details about their life.
While the company reasons that the app is “a new, unique and fun way of showing who you are and of learning more about others,” it’s not hard to imagine that there might be people who will take advantage of this technology and use it to harass and bully unsuspecting individuals.
Tips for helping children deal with cyberbullying
While we can’t protect children from all the dangers that they might face online, the tips below can help you teach them how to deal with cyberbullying:
- Let them know it’s okay to ask for help — If you notice that they’re acting out of sorts (e.g. not sleeping well, refusing to go to school, etc.), let them know that they can talk to you about anything.
- Make them a part of the solution — Involve your kids while you’re trying to resolve the situation. This can help them regain their dignity and sense of control.
- Respond thoughtfully — Parents who don’t think things through can make matters worse for victims of cyberbullying. Always consider any move you make because it can affect your child.
- Listen to both sides of the story — While your instinct might be to believe your child’s side version of the story, try to listen to other accounts as well to get the whole picture.
- Listen to them — It can be hard to discuss situations like this, and sometimes simply having someone to talk to can be a great help to victims of cyberbullying.
You can read more articles about how to use technology wisely at FutureScienceNews.com.
Sources include:
govtslaves.com
by JD Heyes
September 14, 2017
Soon it will be impossible to cover up your face and hide your identity as you engage in a criminal activity, thanks to up-and-coming facial recognition technology.
The bad news is, you won’t be able to hide in plain view either, just to protect your privacy.
As reported by the UK’s Daily Mail, the technology under development has already progressed far enough to virtually “unmask” people in most situations. The Disguised Face Identification (DFI) system employs an AI network as it maps facial features hidden behind scarves, head gear, and even fake beards and mustaches to identify people.
No doubt the system can be integrated with criminal databases so that flagging of wanted people can be done instantaneously; in fact, such systems already exist for automobiles. As Natural News has reported as far back as 2013, police departments have been using license plate readers that allow cops to instantly identify people wanted for various crimes as they drive by their vehicles.
Police aren’t concerned about privacy and the incredible amount of hackable data being collected by the readers. Rather, they’re more concerned with revenues: As the Boston Globe reported in May 2013, one $24,000 plate reader paid for itself in just 11 days. “We located more uninsured vehicles in our first month . . . using [the camera] in one cruiser than the entire department did the whole year before,” said Boston PD Sgt. Robert Griffin.
Now, authorities want to take instant database identification a big step further with new facial recognition technology, which will put a quick end to remaining anonymous in public.
“This is very interesting for law enforcement and other organizations that want to capture criminals,” said Amarjot Singh, a University of Cambridge researcher who helped develop DIF technologies, in an interview with Inverse.
Here’s how the technology works: DFI utilizes a deep-learning AI neural network the research team ‘trained’ by inputting images of test subjects using several different kinds of disguises. In addition, images fed into the network included simple and complex backgrounds that challenged the AI to identify disguised features under a variety of scenarios.
Notes the Daily Mail:
AI identifies people by measuring the distances and angle between 14 facial points — ten for the eyes, three for the lips, and one for the nose.
It uses these readings to estimate the hidden facial structure, and then compares this with learned images to unveil the person’s true identity.
Good, you say. In this age of masked Antifa terrorists, it will be good for police to have the technology to identify who is actually responsible for attacking other people, burning cars, and destroying businesses. (Related: America’s universities now becoming terrorist training hubs for Antifa.)
But what about when the technology misidentifies someone as being guilty of committing a crime or act of violence? Because that’s bound to happen; no technology is 100-percent effective or, in this case, foolproof.
Also, there is so much potential for abuse with this technology. If it is deployed widely, authorities will literally be able to track you no matter where you go.
Plus, this technology dramatically alters the relationship between American citizens and all levels of government. Our founders and subsequent generations established a system of justice that presumes innocence until one can be proven guilty; technologies like this DFI and license plate readers are changing that paradigm from “presumed guilty until authorities can prove you are innocent with a wash through government criminal databases.”
And, of course, there is the dramatic loss of privacy and the threat in the Internet age of having more of your personal information stolen from yet another database.
“…[T]his is maybe the third or fourth most worrying ML paper I’ve seen recently re: AI and emergent authoritarianism. Historical crossroads,” tweeted Dr. Zeynep Tufekci, a sociologist at the University of North Carolina, in posting the research to Twitter.
“Yes, we can & should nitpick this and all papers but the trend is clear. Ever-increasing new capability that will serve authoritarians well,” he added.
J.D. Heyes is a senior writer for NaturalNews.com and NewsTarget.com, as well as editor of The National Sentinel.
Sources include:
Allowing this technology to be used by corrupt, ignorant racist doofus coppers: what could go wrong?
http://www.blacklistednews.com
Source: Boing Boing
Sept 10, 2017
UT Austin sociologist Sarah Brayne spent 2.5 years conducting field research with the LAPD as they rolled out Predpol, a software tool that is supposed to direct police to places where crime is likely to occur, but which has been shown to send cops out to overpolice brown and poor people at the expense of actual crimefighting.
Brayne observed and interviewed more than 75 cops to get a picture of how the job of policing is changed by big data-based “predictive” tools. She found that the tools changed police from a law-enforcement agency to an intelligence agency, concerned more with surveilling people who had not committed a crime than to interdicting or solving crimes in the world.
The cops she interviewed were bullish on Palantir’s products, though they also candidly admitted that predictive tools allowed them to put an objective face on their existing, illegal racial profiling practices (“[Y]ou can’t target individuals especially for any race… [W]e didn’t want to make it look like we’re creating a gang depository of just gang affiliates or gang associates. . . . We were just trying to cover and make sure everything is right on the front end”).
Predictive policing casts a very wide net. Whereas before, the police would only assemble a file on you if you were suspected of a crime, the Palantirization of policing means that “police increasingly utilize data on individuals who have not had any police contact at all.” Tools like the Automatic License Plate Reader log the movements of everyone in a city; then, if a predictive policing algorithm fingers you as being somehow connected to a suspect, all your movements, going far back in time, are summoned up and delivered to the police (the same goes for other automated bulk-collection records, like cellphone surveillance through IMSI catchers and records requests to phone companies).
In Brayne’s words, it’s no longer the case that individuals engage in incriminating acts, now, “individuals lead incriminating lives—daily activities, now codified as data, can be marshaled as evidence ex post facto.”
What’s more, these tools are a ready made for “parallel construction…the process of building a separate evidentiary base for a criminal investigation to conceal how the investigation began, if it involved warrantless surveillance or other inadmissible evidence.” This means that any protections embedded in warrantless surveillance regimes (like the inadmissability of evidence) are easily circumvented by law enforcement.
Brayne paints a picture of law enforcement, Palantir and co working together to keep business-as-usual in place, but with a veneer of empiricism. A cop who “knows” that someone is guilty can cast ever-wider surveillance nets until he finds confirming evidence, then he can rebuild his case using sources that are admissible in court, railroading his chosen perp into prison with the appearance of mathematical objectivity, rather than the racial bias that resulted in the LAPD coming under a Department of Justice consent decree.
As Brayne says, “Characterizing predictive models as ‘just math,’ and fetishizing computation as an objective process, obscures the social side of algorithmic decision-making. Individuals’ interpretation of data occurs in preexisting institutional, legal, and social settings, and it is through that interpretive process that power dynamics come into play.”
This article examines the intersection of two structural developments: the growth of surveillance and the rise of “big data.” Drawing on observations and interviews conducted within the Los Angeles Police Department, I offer an empirical account of how the adoption of big data analytics does—and does not—transform police surveillance practices. I argue that the adoption of big data analytics facilitates amplifications of prior surveillance practices and fundamental transformations in surveillance activities. First, discretionary assessments of risk are supplemented and quantified using risk scores. Second, data are used for predictive, rather than reactive or explanatory, purposes. Third, the proliferation of automatic alert systems makes it possible to systematically surveil an unprecedentedly large number of people. Fourth, the threshold for inclusion in law enforcement databases is lower, now including individuals who have not had direct police contact. Fifth, previously separate data systems are merged, facilitating the spread of surveillance into a wide range of institutions. Based on these findings, I develop a theoretical model of big data surveillance that can be applied to institutional domains beyond the criminal justice system. Finally, I highlight the social consequences of big data surveillance for law and social inequality.
Big Data Surveillance: The Case of Policing [Sarah Brayne/American Sociological Review] (Sci-Hub mirror)
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