Entries Tagged "reports"
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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.
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.
The International Committee of the Red Cross has just published a report: “The Potential Human Cost of Cyber-Operations.” It’s the result of an “ICRC Expert Meeting” from last year, but was published this week.
Gregory C. Allen at the Center for a New American Security has a new report with some interesting analysis and insights into China’s AI strategy, commercial, government, and military. There are numerous security — and national security — implications.
Construction cranes are vulnerable to hacking:
In our research and vulnerability discoveries, we found that weaknesses in the controllers can be (easily) taken advantage of to move full-sized machines such as cranes used in construction sites and factories. In the different attack classes that we’ve outlined, we were able to perform the attacks quickly and even switch on the controlled machine despite an operator’s having issued an emergency stop (e-stop).
The core of the problem lies in how, instead of depending on wireless, standard technologies, these industrial remote controllers rely on proprietary RF protocols, which are decades old and are primarily focused on safety at the expense of security. It wasn’t until the arrival of Industry 4.0, as well as the continuing adoption of the industrial internet of things (IIoT), that industries began to acknowledge the pressing need for security.
The US House of Representatives Committee on Oversight and Government Reform has just released a comprehensive report on the 2017 Equifax hack. It’s a great piece of writing, with a detailed timeline, root cause analysis, and lessons learned. Lance Spitzner also commented on this.
Here is my testimony before before the House Subcommittee on Digital Commerce and Consumer Protection last November.
Interesting policy paper by Third Way: “To Catch a Hacker: Toward a comprehensive strategy to identify, pursue, and punish malicious cyber actors“:
In this paper, we argue that the United States currently lacks a comprehensive overarching strategic approach to identify, stop and punish cyberattackers. We show that:
- There is a burgeoning cybercrime wave: A rising and often unseen crime wave is mushrooming in America. There are approximately 300,000 reported malicious cyber incidents per year, including up to 194,000 that could credibly be called individual or system-wide breaches or attempted breaches. This is likely a vast undercount since many victims don’t report break-ins to begin with. Attacks cost the US economy anywhere from $57 billion to $109 billion annually and these costs are increasing.
- There is a stunning cyber enforcement gap: Our analysis of publicly available data shows that cybercriminals can operate with near impunity compared to their real-world counterparts. We estimate that cyber enforcement efforts are so scattered that less than 1% of malicious cyber incidents see an enforcement action taken against the attackers.
- There is no comprehensive US cyber enforcement strategy aimed at the human attacker: Despite the recent release of a National Cyber Strategy, the United States still lacks a comprehensive strategic approach to how it identifies, pursues, and punishes malicious human cyberattackers and the organizations and countries often behind them. We believe that the United States is as far from this human attacker strategy as the nation was toward a strategic approach to countering terrorism in the weeks and months before 9/11.
In order to close the cyber enforcement gap, we argue for a comprehensive enforcement strategy that makes a fundamental rebalance in US cybersecurity policies: from a heavy focus on building better cyber defenses against intrusion to also waging a more robust effort at going after human attackers. We call for ten US policy actions that could form the contours of a comprehensive enforcement strategy to better identify, pursue and bring to justice malicious cyber actors that include building up law enforcement, enhancing diplomatic efforts, and developing a measurable strategic plan to do so.
Sidebar photo of Bruce Schneier by Joe MacInnis.