Tag Archives: cameras

Ring Gives Videos to Police without a Warrant or User Consent

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/08/ring-gives-videos-to-police-without-a-warrant-or-user-consent.html

Amazon has revealed that it gives police videos from its Ring doorbells without a warrant and without user consent.

Ring recently revealed how often the answer to that question has been yes. The Amazon company responded to an inquiry from US Senator Ed Markey (D-Mass.), confirming that there have been 11 cases in 2022 where Ring complied with police “emergency” requests. In each case, Ring handed over private recordings, including video and audio, without letting users know that police had access to—and potentially downloaded—their data. This raises many concerns about increased police reliance on private surveillance, a practice that has long gone unregulated.

EFF writes:

Police are not the customers for Ring; the people who buy the devices are the customers. But Amazon’s long-standing relationships with police blur that line. For example, in the past Amazon has given coaching to police to tell residents to install the Ring app and purchase cameras for their homes—­an arrangement that made salespeople out of the police force. The LAPD launched an investigation into how Ring provided free devices to officers when people used their discount codes to purchase cameras.

Ring, like other surveillance companies that sell directly to the general public, continues to provide free services to the police, even though they don’t have to. Ring could build a device, sold straight to residents, that ensures police come to the user’s door if they are interested in footage—­but Ring instead has decided it would rather continue making money from residents while providing services to police.

CNet has a good explainer.

Slashdot thread.

San Francisco Police Want Real-Time Access to Private Surveillance Cameras

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/07/san-francisco-police-want-real-time-access-to-private-surveillance-cameras.html

Surely no one could have predicted this:

The new proposal—championed by Mayor London Breed after November’s wild weekend of orchestrated burglaries and theft in the San Francisco Bay Area—would authorize the police department to use non-city-owned security cameras and camera networks to live monitor “significant events with public safety concerns” and ongoing felony or misdemeanor violations.

Currently, the police can only request historical footage from private cameras related to specific times and locations, rather than blanket monitoring. Mayor Breed also complained the police can only use real-time feeds in emergencies involving “imminent danger of death or serious physical injury.”

If approved, the draft ordinance would also allow SFPD to collect historical video footage to help conduct criminal investigations and those related to officer misconduct. The draft law currently stands as the following, which indicates the cops can broadly ask for and/or get access to live real-time video streams:

The proposed Surveillance Technology Policy would authorize the Police Department to use surveillance cameras and surveillance camera networks owned, leased, managed, or operated by non-City entities to: (1) temporarily live monitor activity during exigent circumstances, significant events with public safety concerns, and investigations relating to active misdemeanor and felony violations; (2) gather and review historical video footage for the purposes of conducting a criminal investigation; and (3) gather and review historical video footage for the purposes of an internal investigation regarding officer misconduct.

Wyze Camera Vulnerability

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/04/wyze-camera-vulnerability.html

Wyze ignored a vulnerability in its home security cameras for three years. Bitdefender, who discovered the vulnerability, let the company get away with it.

In case you’re wondering, no, that is not normal in the security community. While experts tell me that the concept of a “responsible disclosure timeline” is a little outdated and heavily depends on the situation, we’re generally measuring in days, not years. “The majority of researchers have policies where if they make a good faith effort to reach a vendor and don’t get a response, that they publicly disclose in 30 days,” Alex Stamos, director of the Stanford Internet Observatory and former chief security officer at Facebook, tells me.

Modern Mass Surveillance: Identify, Correlate, Discriminate

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/01/modern_mass_sur.html

Communities across the United States are starting to ban facial recognition technologies. In May of last year, San Francisco banned facial recognition; the neighboring city of Oakland soon followed, as did Somerville and Brookline in Massachusetts (a statewide ban may follow). In December, San Diego suspended a facial recognition program in advance of a new statewide law, which declared it illegal, coming into effect. Forty major music festivals pledged not to use the technology, and activists are calling for a nationwide ban. Many Democratic presidential candidates support at least a partial ban on the technology.

These efforts are well-intentioned, but facial recognition bans are the wrong way to fight against modern surveillance. Focusing on one particular identification method misconstrues the nature of the surveillance society we’re in the process of building. Ubiquitous mass surveillance is increasingly the norm. In countries like China, a surveillance infrastructure is being built by the government for social control. In countries like the United States, it’s being built by corporations in order to influence our buying behavior, and is incidentally used by the government.

In all cases, modern mass surveillance has three broad components: identification, correlation and discrimination. Let’s take them in turn.

Facial recognition is a technology that can be used to identify people without their knowledge or consent. It relies on the prevalence of cameras, which are becoming both more powerful and smaller, and machine learning technologies that can match the output of these cameras with images from a database of existing photos.

But that’s just one identification technology among many. People can be identified at a distance by their heartbeat or by their gait, using a laser-based system. Cameras are so good that they can read fingerprints and iris patterns from meters away. And even without any of these technologies, we can always be identified because our smartphones broadcast unique numbers called MAC addresses. Other things identify us as well: our phone numbers, our credit card numbers, the license plates on our cars. China, for example, uses multiple identification technologies to support its surveillance state.

Once we are identified, the data about who we are and what we are doing can be correlated with other data collected at other times. This might be movement data, which can be used to “follow” us as we move throughout our day. It can be purchasing data, Internet browsing data, or data about who we talk to via email or text. It might be data about our income, ethnicity, lifestyle, profession and interests. There is an entire industry of data brokers who make a living analyzing and augmenting data about who we are ­– using surveillance data collected by all sorts of companies and then sold without our knowledge or consent.

There is a huge ­– and almost entirely unregulated ­– data broker industry in the United States that trades on our information. This is how large Internet companies like Google and Facebook make their money. It’s not just that they know who we are, it’s that they correlate what they know about us to create profiles about who we are and what our interests are. This is why many companies buy license plate data from states. It’s also why companies like Google are buying health records, and part of the reason Google bought the company Fitbit, along with all of its data.

The whole purpose of this process is for companies –­ and governments ­– to treat individuals differently. We are shown different ads on the Internet and receive different offers for credit cards. Smart billboards display different advertisements based on who we are. In the future, we might be treated differently when we walk into a store, just as we currently are when we visit websites.

The point is that it doesn’t matter which technology is used to identify people. That there currently is no comprehensive database of heartbeats or gaits doesn’t make the technologies that gather them any less effective. And most of the time, it doesn’t matter if identification isn’t tied to a real name. What’s important is that we can be consistently identified over time. We might be completely anonymous in a system that uses unique cookies to track us as we browse the Internet, but the same process of correlation and discrimination still occurs. It’s the same with faces; we can be tracked as we move around a store or shopping mall, even if that tracking isn’t tied to a specific name. And that anonymity is fragile: If we ever order something online with a credit card, or purchase something with a credit card in a store, then suddenly our real names are attached to what was anonymous tracking information.

Regulating this system means addressing all three steps of the process. A ban on facial recognition won’t make any difference if, in response, surveillance systems switch to identifying people by smartphone MAC addresses. The problem is that we are being identified without our knowledge or consent, and society needs rules about when that is permissible.

Similarly, we need rules about how our data can be combined with other data, and then bought and sold without our knowledge or consent. The data broker industry is almost entirely unregulated; there’s only one law ­– passed in Vermont in 2018 ­– that requires data brokers to register and explain in broad terms what kind of data they collect. The large Internet surveillance companies like Facebook and Google collect dossiers on us are more detailed than those of any police state of the previous century. Reasonable laws would prevent the worst of their abuses.

Finally, we need better rules about when and how it is permissible for companies to discriminate. Discrimination based on protected characteristics like race and gender is already illegal, but those rules are ineffectual against the current technologies of surveillance and control. When people can be identified and their data correlated at a speed and scale previously unseen, we need new rules.

Today, facial recognition technologies are receiving the brunt of the tech backlash, but focusing on them misses the point. We need to have a serious conversation about all the technologies of identification, correlation and discrimination, and decide how much we as a society want to be spied on by governments and corporations — and what sorts of influence we want them to have over our lives.

This essay previously appeared in the New York Times.

EDITED TO ADD: Rereading this post-publication, I see that it comes off as overly critical of those who are doing activism in this space. Writing the piece, I wasn’t thinking about political tactics. I was thinking about the technologies that support surveillance capitalism, and law enforcement’s usage of that corporate platform. Of course it makes sense to focus on face recognition in the short term. It’s something that’s easy to explain, viscerally creepy, and obviously actionable. It also makes sense to focus specifically on law enforcement’s use of the technology; there are clear civil and constitutional rights issues. The fact that law enforcement is so deeply involved in the technology’s marketing feels wrong. And the technology is currently being deployed in Hong Kong against political protesters. It’s why the issue has momentum, and why we’ve gotten the small wins we’ve had. (The EU is considering a five-year ban on face recognition technologies.) Those wins build momentum, which lead to more wins. I should have been kinder to those in the trenches.

If you want to help, sign the petition from Public Voice calling on a moratorium on facial recognition technology for mass surveillance. Or write to your US congressperson and demand similar action. There’s more information from EFF and EPIC.

Police Surveillance Tools from Special Services Group

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/01/police_surveill.html

Special Services Group, a company that sells surveillance tools to the FBI, DEA, ICE, and other US government agencies, has had its secret sales brochure published. Motherboard received the brochure as part of a FOIA request to the Irvine Police Department in California.

“The Tombstone Cam is our newest video concealment offering the ability to conduct remote surveillance operations from cemeteries,” one section of the Black Book reads. The device can also capture audio, its battery can last for two days, and “the Tombstone Cam is fully portable and can be easily moved from location to location as necessary,” the brochure adds. Another product is a video and audio capturing device that looks like an alarm clock, suitable for “hotel room stings,” and other cameras are designed to appear like small tree trunks and rocks, the brochure reads.

The “Shop-Vac Covert DVR Recording System” is essentially a camera and 1TB harddrive hidden inside a vacuum cleaner. “An AC power connector is available for long-term deployments, and DC power options can be connected for mobile deployments also,” the brochure reads. The description doesn’t say whether the vacuum cleaner itself works.

[…]

One of the company’s “Rapid Vehicle Deployment Kits” includes a camera hidden inside a baby car seat. “The system is fully portable, so you are not restricted to the same drop car for each mission,” the description adds.

[…]

The so-called “K-MIC In-mouth Microphone & Speaker Set” is a tiny Bluetooth device that sits on a user’s teeth and allows them to “communicate hands-free in crowded, noisy surroundings” with “near-zero visual indications,” the Black Book adds.

Other products include more traditional surveillance cameras and lenses as well as tools for surreptitiously gaining entry to buildings. The “Phantom RFID Exploitation Toolkit” lets a user clone an access card or fob, and the so-called “Shadow” product can “covertly provide the user with PIN code to an alarm panel,” the brochure reads.

The Motherboard article also reprints the scary emails Motherboard received from Special Services Group, when asked for comment. Of course, Motherboard published the information anyway.

Zoom Vulnerability

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/07/zoom_vulnerabil.html

The Zoom conferencing app has a vulnerability that allows someone to remotely take over the computer’s camera.

It’s a bad vulnerability, made worse by the fact that it remains even if you uninstall the Zoom app:

This vulnerability allows any website to forcibly join a user to a Zoom call, with their video camera activated, without the user’s permission.

On top of this, this vulnerability would have allowed any webpage to DOS (Denial of Service) a Mac by repeatedly joining a user to an invalid call.

Additionally, if you’ve ever installed the Zoom client and then uninstalled it, you still have a localhost web server on your machine that will happily re-install the Zoom client for you, without requiring any user interaction on your behalf besides visiting a webpage. This re-install ‘feature’ continues to work to this day.

Zoom didn’t take the vulnerability seriously:

This vulnerability was originally responsibly disclosed on March 26, 2019. This initial report included a proposed description of a ‘quick fix’ Zoom could have implemented by simply changing their server logic. It took Zoom 10 days to confirm the vulnerability. The first actual meeting about how the vulnerability would be patched occurred on June 11th, 2019, only 18 days before the end of the 90-day public disclosure deadline. During this meeting, the details of the vulnerability were confirmed and Zoom’s planned solution was discussed. However, I was very easily able to spot and describe bypasses in their planned fix. At this point, Zoom was left with 18 days to resolve the vulnerability. On June 24th after 90 days of waiting, the last day before the public disclosure deadline, I discovered that Zoom had only implemented the ‘quick fix’ solution originally suggested.

This is why we disclose vulnerabilities. Now, finally, Zoom is taking this seriously and fixing it for real.

Computers and Video Surveillance

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/06/computers_and_video.html

It used to be that surveillance cameras were passive. Maybe they just recorded, and no one looked at the video unless they needed to. Maybe a bored guard watched a dozen different screens, scanning for something interesting. In either case, the video was only stored for a few days because storage was expensive.

Increasingly, none of that is true. Recent developments in video analytics — fueled by artificial intelligence techniques like machine learning — enable computers to watch and understand surveillance videos with human-like discernment. Identification technologies make it easier to automatically figure out who is in the videos. And finally, the cameras themselves have become cheaper, more ubiquitous, and much better; cameras mounted on drones can effectively watch an entire city. Computers can watch all the video without human issues like distraction, fatigue, training, or needing to be paid. The result is a level of surveillance that was impossible just a few years ago.

An ACLU report published Thursday called “the Dawn of Robot Surveillance” says AI-aided video surveillance “won’t just record us, but will also make judgments about us based on their understanding of our actions, emotions, skin color, clothing, voice, and more. These automated ‘video analytics’ technologies threaten to fundamentally change the nature of surveillance.”

Let’s take the technologies one at a time. First: video analytics. Computers are getting better at recognizing what’s going on in a video. Detecting when a person or vehicle enters a forbidden area is easy. Modern systems can alarm when someone is walking in the wrong direction — going in through an exit-only corridor, for example. They can count people or cars. They can detect when luggage is left unattended, or when previously unattended luggage is picked up and removed. They can detect when someone is loitering in an area, is lying down, or is running. Increasingly, they can detect particular actions by people. Amazon’s cashier-less stores rely on video analytics to figure out when someone picks an item off a shelf and doesn’t put it back.

More than identifying actions, video analytics allow computers to understand what’s going on in a video: They can flag people based on their clothing or behavior, identify people’s emotions through body language and behavior, and find people who are acting “unusual” based on everyone else around them. Those same Amazon in-store cameras can analyze customer sentiment. Other systems can describe what’s happening in a video scene.

Computers can also identify people. AIs are getting better at identifying people in those videos. Facial recognition technology is improving all the time, made easier by the enormous stockpile of tagged photographs we give to Facebook and other social media sites, and the photos governments collect in the process of issuing ID cards and drivers licenses. The technology already exists to automatically identify everyone a camera “sees” in real time. Even without video identification, we can be identified by the unique information continuously broadcasted by the smartphones we carry with us everywhere, or by our laptops or Bluetooth-connected devices. Police have been tracking phones for years, and this practice can now be combined with video analytics.

Once a monitoring system identifies people, their data can be combined with other data, either collected or purchased: from cell phone records, GPS surveillance history, purchasing data, and so on. Social media companies like Facebook have spent years learning about our personalities and beliefs by what we post, comment on, and “like.” This is “data inference,” and when combined with video it offers a powerful window into people’s behaviors and motivations.

Camera resolution is also improving. Gigapixel cameras as so good that they can capture individual faces and identify license places in photos taken miles away. “Wide-area surveillance” cameras can be mounted on airplanes and drones, and can operate continuously. On the ground, cameras can be hidden in street lights and other regular objects. In space, satellite cameras have also dramatically improved.

Data storage has become incredibly cheap, and cloud storage makes it all so easy. Video data can easily be saved for years, allowing computers to conduct all of this surveillance backwards in time.

In democratic countries, such surveillance is marketed as crime prevention — or counterterrorism. In countries like China, it is blatantly used to suppress political activity and for social control. In all instances, it’s being implemented without a lot of public debate by law-enforcement agencies and by corporations in public spaces they control.

This is bad, because ubiquitous surveillance will drastically change our relationship to society. We’ve never lived in this sort of world, even those of us who have lived through previous totalitarian regimes. The effects will be felt in many different areas. False positives­ — when the surveillance system gets it wrong­ — will lead to harassment and worse. Discrimination will become automated. Those who fall outside norms will be marginalized. And most importantly, the inability to live anonymously will have an enormous chilling effect on speech and behavior, which in turn will hobble society’s ability to experiment and change. A recent ACLU report discusses these harms in more depth. While it’s possible that some of this surveillance is worth the trade-offs, we as society need to deliberately and intelligently make decisions about it.

Some jurisdictions are starting to notice. Last month, San Francisco became the first city to ban facial recognition technology by police and other government agencies. A similar ban is being considered in Somerville, MA, and Oakland, CA. These are exceptions, and limited to the more liberal areas of the country.

We often believe that technological change is inevitable, and that there’s nothing we can do to stop it — or even to steer it. That’s simply not true. We’re led to believe this because we don’t often see it, understand it, or have a say in how or when it is deployed. The problem is that technologies of cameras, resolution, machine learning, and artificial intelligence are complex and specialized.

Laws like what was just passed in San Francisco won’t stop the development of these technologies, but they’re not intended to. They’re intended as pauses, so our policy making can catch up with technology. As a general rule, the US government tends to ignore technologies as they’re being developed and deployed, so as not to stifle innovation. But as the rate of technological change increases, so does the unanticipated effects on our lives. Just as we’ve been surprised by the threats to democracy caused by surveillance capitalism, AI-enabled video surveillance will have similar surprising effects. Maybe a pause in our headlong deployment of these technologies will allow us the time to discuss what kind of society we want to live in, and then enact rules to bring that kind of society about.

This essay previously appeared on Vice Motherboard.

Video Surveillance by Computer

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/06/video_surveilla.html

The ACLU’s Jay Stanley has just published a fantastic report: “The Dawn of Robot Surveillance” (blog post here) Basically, it lays out a future of ubiquitous video cameras watched by increasingly sophisticated video analytics software, and discusses the potential harms to society.

I’m not going to excerpt a piece, because you really need to read the whole thing.

iOS Shortcut for Recording the Police

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/06/ios_shortcut_fo.html

Hey Siri; I’m getting pulled over” can be a shortcut:

Once the shortcut is installed and configured, you just have to say, for example, “Hey Siri, I’m getting pulled over.” Then the program pauses music you may be playing, turns down the brightness on the iPhone, and turns on “do not disturb” mode.

It also sends a quick text to a predetermined contact to tell them you’ve been pulled over, and it starts recording using the iPhone’s front-facing camera. Once you’ve stopped recording, it can text or email the video to a different predetermined contact and save it to Dropbox.

Hidden Cameras in Streetlights

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/11/hidden_cameras_.html

Both the US Drug Enforcement Administration (DEA) and Immigration and Customs Enforcement (ICE) are hiding surveillance cameras in streetlights.

According to government procurement data, the DEA has paid a Houston, Texas company called Cowboy Streetlight Concealments LLC roughly $22,000 since June 2018 for “video recording and reproducing equipment.” ICE paid out about $28,000 to Cowboy Streetlight Concealments over the same period of time.

It’s unclear where the DEA and ICE streetlight cameras have been installed, or where the next deployments will take place. ICE offices in Dallas, Houston, and San Antonio have provided funding for recent acquisitions from Cowboy Streetlight Concealments; the DEA’s most recent purchases were funded by the agency’s Office of Investigative Technology, which is located in Lorton, Virginia.

Fifty thousand dollars doesn’t buy a lot of streetlight surveillance cameras, so either this is a pilot program or there are a lot more procurements elsewhere that we don’t know about.

Consumer Reports Reviews Wireless Home-Security Cameras

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/11/consumer_report_1.html

Consumer Reports is starting to evaluate the security of IoT devices. As part of that, it’s reviewing wireless home-security cameras.

It found significant security vulnerabilities in D-Link cameras:

In contrast, D-Link doesn’t store video from the DCS-2630L in the cloud. Instead, the camera has its own, onboard web server, which can deliver video to the user in different ways.

Users can view the video using an app, mydlink Lite. The video is encrypted, and it travels from the camera through D-Link’s corporate servers, and ultimately to the user’s phone. Users can also access the same encrypted video feed through a company web page, mydlink.com. Those are both secure methods of accessing the video.

But the D-Link camera also lets you bypass the D-Link corporate servers and access the video directly through a web browser on a laptop or other device. If you do this, the web server on the camera doesn’t encrypt the video.

If you set up this kind of remote access, the camera and unencrypted video is open to the web. They could be discovered by anyone who finds or guesses the camera’s IP address­ — and if you haven’t set a strong password, a hacker might find it easy to gain access.

The real news is that Consumer Reports is able to put pressure on device manufacturers:

In response to a Consumer Reports query, D-Link said that security would be tightened through updates this fall. Consumer Reports will evaluate those updates once they are available.

This is the sort of sustained pressure we need on IoT device manufacturers.

Boing Boing link.

EDITED TO ADD (11/13): In related news, the US Federal Trade Commission is suing D-Link because their routers are so insecure. The lawsuit was filed in January 2017.

Take a photo of yourself as an unreliable cartoon

Post Syndicated from Helen Lynn original https://www.raspberrypi.org/blog/take-a-photo-of-yourself-unreliable-cartoon/

Take a selfie, wait for the image to appear, and behold a cartoon version of yourself. Or, at least, behold a cartoon version of whatever the camera thought it saw. Welcome to Draw This by maker Dan Macnish.

Dan has made code, instructions, and wiring diagrams available to help you bring this beguiling weirdery into your own life.

raspberry pi cartoon polaroid camera

Neural networks, object recognition, and cartoons

One of the fun things about this re-imagined polaroid is that you never get to see the original image. You point, and shoot – and out pops a cartoon; the camera’s best interpretation of what it saw. The result is always a surprise. A food selfie of a healthy salad might turn into an enormous hot dog, or a photo with friends might be photobombed by a goat.

OK. Let’s take this one step at a time.

Pi + camera + button + LED

Draw This uses a Raspberry Pi 3 and a Camera Module, with a button and a useful status LED connected to the GPIO pins via a breadboard. You press the button, and the camera captures a still image while the LED comes on and stays lit for a couple of seconds while the Pi processes the image. So far, so standard Pi camera build.

Interpreting and re-interpreting the camera image

Dan uses Python to process the captured photograph, employing a pre-trained machine learning model from Google to recognise multiple objects in the image. Now he brings the strangeness. The Pi matches the things it sees in the photo with doodles from Google’s huge open-source Quick, Draw! dataset, and generates a new image that represents the objects in the original image as doodles. Then a thermal printer connected to the Pi’s GPIO pins prints the results.

A 28 x 14 grid of kangaroo doodles in dark grey on a white background

Kangaroos from the Quick, Draw! dataset (I got distracted)

Potential for peculiar

Reading about this build leaves me yearning to see its oddest interpretation of a scene, so if you make this and you find it really does turn you or your friend into a goat, please do share that with us.

And as you can see from my kangaroo digression above, there is a ton of potential for bizarro makes that use the Quick, Draw! dataset, object recognition models, or both; it’s not just the marsupials that are inexplicably compelling (I dare you to go and look and see how long it takes you to get back to whatever you were in the middle of). If you’re planning to make this, or something inspired by this, check out Dan’s cartoonify GitHub repo. And tell us all about it in the comments.

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Working with the Scout Association on digital skills for life

Post Syndicated from Philip Colligan original https://www.raspberrypi.org/blog/working-with-scout-association-digital-skills-for-life/

Today we’re launching a new partnership between the Scouts and the Raspberry Pi Foundation that will help tens of thousands of young people learn crucial digital skills for life. In this blog post, I want to explain what we’ve got planned, why it matters, and how you can get involved.

This is personal

First, let me tell you why this partnership matters to me. As a child growing up in North Wales in the 1980s, Scouting changed my life. My time with 2nd Rhyl provided me with countless opportunities to grow and develop new skills. It taught me about teamwork and community in ways that continue to shape my decisions today.

As my own kids (now seven and ten) have joined Scouting, I’ve seen the same opportunities opening up for them, and like so many parents, I’ve come back to the movement as a volunteer to support their local section. So this is deeply personal for me, and the same is true for many of my colleagues at the Raspberry Pi Foundation who in different ways have been part of the Scouting movement.

That shouldn’t come as a surprise. Scouting and Raspberry Pi share many of the same values. We are both community-led movements that aim to help young people develop the skills they need for life. We are both powered by an amazing army of volunteers who give their time to support that mission. We both care about inclusiveness, and pride ourselves on combining fun with learning by doing.

Raspberry Pi

Raspberry Pi started life in 2008 as a response to the problem that too many young people were growing up without the skills to create with technology. Our goal is that everyone should be able to harness the power of computing and digital technologies, for work, to solve problems that matter to them, and to express themselves creatively.

In 2012 we launched our first product, the world’s first $35 computer. Just six years on, we have sold over 20 million Raspberry Pi computers and helped kickstart a global movement for digital skills.

The Raspberry Pi Foundation now runs the world’s largest network of volunteer-led computing clubs (Code Clubs and CoderDojos), and creates free educational resources that are used by millions of young people all over the world to learn how to create with digital technologies. And lots of what we are able to achieve is because of partnerships with fantastic organisations that share our goals. For example, through our partnership with the European Space Agency, thousands of young people have written code that has run on two Raspberry Pi computers that Tim Peake took to the International Space Station as part of his Mission Principia.

Digital makers

Today we’re launching the new Digital Maker Staged Activity Badge to help tens of thousands of young people learn how to create with technology through Scouting. Over the past few months, we’ve been working with the Scouts all over the UK to develop and test the new badge requirements, along with guidance, project ideas, and resources that really make them work for Scouting. We know that we need to get two things right: relevance and accessibility.

Relevance is all about making sure that the activities and resources we provide are a really good fit for Scouting and Scouting’s mission to equip young people with skills for life. From the digital compass to nature cameras and the reinvented wide game, we’ve had a lot of fun thinking about ways we can bring to life the crucial role that digital technologies can play in the outdoors and adventure.

Compass Coding with Raspberry Pi

We are beyond excited to be launching a new partnership with the Raspberry Pi Foundation, which will help tens of thousands of young people learn digital skills for life.

We also know that there are great opportunities for Scouts to use digital technologies to solve social problems in their communities, reflecting the movement’s commitment to social action. Today we’re launching the first set of project ideas and resources, with many more to follow over the coming weeks and months.

Accessibility is about providing every Scout leader with the confidence, support, and kit to enable them to offer the Digital Maker Staged Activity Badge to their young people. A lot of work and care has gone into designing activities that require very little equipment: for example, activities at Stages 1 and 2 can be completed with a laptop without access to the internet. For the activities that do require kit, we will be working with Scout Stores and districts to make low-cost kit available to buy or loan.

We’re producing accessible instructions, worksheets, and videos to help leaders run sessions with confidence, and we’ll also be planning training for leaders. We will work with our network of Code Clubs and CoderDojos to connect them with local sections to organise joint activities, bringing both kit and expertise along with them.




Get involved

Today’s launch is just the start. We’ll be developing our partnership over the next few years, and we can’t wait for you to join us in getting more young people making things with technology.

Take a look at the brand-new Raspberry Pi resources designed especially for Scouts, to get young people making and creating right away.

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Lifting a Fingerprint from a Photo

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/04/lifting_a_finge.html

Police in the UK were able to read a fingerprint from a photo of a hand:

Staff from the unit’s specialist imaging team were able to enhance a picture of a hand holding a number of tablets, which was taken from a mobile phone, before fingerprint experts were able to positively identify that the hand was that of Elliott Morris.

[…]

Speaking about the pioneering techniques used in the case, Dave Thomas, forensic operations manager at the Scientific Support Unit, added: “Specialist staff within the JSIU fully utilised their expert image-enhancing skills which enabled them to provide something that the unit’s fingerprint identification experts could work. Despite being provided with only a very small section of the fingerprint which was visible in the photograph, the team were able to successfully identify the individual.”

Build a solar-powered nature camera for your garden

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/solar-powered-nature-camera/

Spring has sprung, and with it, sleepy-eyed wildlife is beginning to roam our gardens and local woodlands. So why not follow hackster.io maker reichley’s tutorial and build your own solar-powered squirrelhouse nature cam?

Raspberry Pi- and solar-powered nature camera

Inspiration

“I live half a mile above sea level and am SURROUNDED by animals…bears, foxes, turkeys, deer, squirrels, birds”, reichley explains in his tutorial. “Spring has arrived, and there are LOADS of squirrels running around. I was in the building mood and, being a nerd, wished to combine a common woodworking project with the connectivity and observability provided by single-board computers (and their camera add-ons).”

Building a tiny home

reichley started by sketching out a design for the house to determine where the various components would fit.

Raspberry Pi- and solar-powered nature camera

Since he’s fan of autonomy and renewable energy, he decided to run the project’s Raspberry Pi Zero W via solar power. To do so, he reiterated the design to include the necessary tech, scaling the roof to fit the panels.

Raspberry Pi- and solar-powered squirrel cam
Raspberry Pi- and solar-powered squirrel cam
Raspberry Pi- and solar-powered squirrel cam

To keep the project running 24/7, reichley had to figure out the overall power consumption of both the Zero W and the Raspberry Pi Camera Module, factoring in the constant WiFi connection and the sunshine hours in his garden.

Raspberry Pi- and solar-powered nature camera

He used a LiPo SHIM to bump up the power to the required 5V for the Zero. Moreover, he added a BH1750 lux sensor to shut off the LiPo SHIM, and thus the Pi, whenever it’s too dark for decent video.

Raspberry Pi- and solar-powered nature camera

To control the project, he used Calin Crisan’s motionEyeOS video surveillance operating system for single-board computers.

Build your own nature camera

To build your own version, follow reichley’s tutorial, in which you can also find links to all the necessary code and components. You can also check out our free tutorial for building an infrared bird box using the Raspberry Pi NoIR Camera Module. As Eben said in our YouTube live Q&A last week, we really like nature cameras here at Pi Towers, and we’d love to see yours. So if you have any live-stream links or photography from your Raspberry Pi–powered nature cam, please share them with us!

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