Net surveillance web download file error

China is exporting surveillance tech to liberal democracies as much as it is targeting authoritarian markets. Likewise, companies based in liberal democracies for example, Germany, France, Israel, Japan, South Korea, the UK, the United States are actively selling sophisticated equipment to unsavory regimes. Saudi Arabia is a good case in point. Huawei is helping the government build safe cities, but Google is establishing cloud servers, UK arms manufacturer BAE has sold mass surveillance systems, NEC is vending facial recognition cameras, and Amazon and Alibaba both have cloud computing centers in Saudi Arabia and may support a major smart city project.

We compromise, we negotiate, and we balance. That being said, there are special reasons why experts are applying greater scrutiny to Chinese companies. Huawei is the leading vendor of advanced surveillance systems worldwide by a huge factor. Its technology is linked to more countries in the index than any other company.

It is aggressively seeking new markets in regions like sub-Saharan Africa. Huawei is not only providing advanced equipment but also offering ongoing technological support to set up, operate, and manage these systems. A recent investigative report by the Wall Street Journal provides an eye-opening example. The reporters found that Huawei technicians in both Uganda and Zambia helped government officials spy on political opponents. The result is that a country like Mauritius obtains long-term financing from the Chinese government, which mandates contracting with Chinese firms.

It is also increasingly clear that firms such as Huawei operate with far less independence from the Chinese government than they claim. A recent academic study by Christopher Balding and Donald C. Even if Chinese companies are making a greater push to sell advanced surveillance tech, the issue of intentionality remains perplexing—to what extent are Chinese firms like Huawei and ZTE operating out of their own economic self-interest when peddling surveillance technology versus carrying out the bidding of the Chinese state?

At least in Thailand, recent research interviews did not turn up indications that Chinese companies are pushing a concerted agenda to peddle advanced AI surveillance equipment or encourage the government to build sophisticated monitoring systems.

He told me that we are thinking about giving wristbands to tourists so that we can track them, we can help them. Something like that. Smart city in Phuket turns out to be providing free Wi-Fi and internet to tourists!

The following sections will describe key AI surveillance techniques and how governments worldwide are deploying them to support specific policy objectives. States use AI technology to accomplish a broad range of surveillance goals. It also describes enabling technologies—such as cloud computing and Internet of Things IOT networks—that are integral for AI surveillance tools to function.

Enabling technologies are not incorporated in the index. Importantly, AI surveillance is not a standalone instrument of repression. It forms part of a suite of digital repression tools—information and communications technologies used to surveil, intimidate, coerce, and harass opponents in order to inflict a penalty on a target and deter specific activities or beliefs that challenge the state. Table 1 summarizes each technique and its corresponding level of global deployment.

But there is growing concern that smart cities are also enabling a dramatic increase in public surveillance and intrusive security capabilities. IBM, one of the original coiners of the term, designed a brain-like municipal model where information relevant to city operations could be centrally processed and analyzed.

Huawei has been up-front about trumpeting public safety technologies for smart cities. How do these platforms work in practice to advance surveillance goals? The IT firm Gartner, which partners with Microsoft on smart cities, provides an example:. Data is collected via a wristband embedding identity information, special healthcare requirements and a GPS. Unsurprisingly, such systems lend themselves to improper use. Smart city platforms with a direct public security link are found in at least fifty-six of seventy-five countries with AI surveillance technology.

Facial recognition is a biometric technology that uses cameras—both video or still images—to match stored or live footage of individuals with images from a database. Not all facial recognition systems focus on individual identification via database matching. Some systems are designed to assess aggregate demographic trends or to conduct broader sentiment analysis via facial recognition crowd scanning. Unlike ordinary CCTV, which has been a mainstay of police forces for twenty-five years, facial recognition cameras are much more intrusive.

They can scan distinctive facial features in order to create detailed biometric maps of individuals without obtaining consent. Often facial recognition surveillance cameras are mobile and concealable. For example, security forces in Malaysia have entered into a partnership with the Chinese tech company Yitu to equip officers with facial recognition body cameras.

Huawei is a major purveyor of facial recognition video surveillance, particularly as part of its safe city platforms. A national police command center supporting over 9, police officers and police stations was established to achieve monitoring and case-solving. First, few rules govern access to and the use of image databases repositories that store captured images from facial recognition cameras. How governments use this information, how long images are stored, and where authorities obtain such images in the first place are opaque issues and vary by jurisdiction.

Recent disclosures that U. Second, the accuracy of facial recognition technology varies significantly. Certain tests have disclosed unacceptably high false-match rates. But other assessments demonstrate much more favorable results.

Evaluations conducted between and of algorithms from thirty-nine developers by the U. One reason for the discrepancy is that under ideal conditions, facial recognition can perform very well. But when unexpected variables are thrown in—poor weather or fuzzy database images—then failure rates start to shoot up.

Citizens are starting to fight back against facial recognition systems. Protesters in Hong Kong, for example, have covered up their faces and disabled their smartphone facial recognition logins to prevent law enforcement access.

Facial recognition systems are rapidly spreading around the world. The index identifies at least sixty-four countries that are actively incorporating facial recognition systems in their AI surveillance programs.

The idea behind smart policing is to feed immense quantities of data into an algorithm—geographic location, historic arrest levels, types of committed crimes, biometric data, social media feeds—in order to prevent crime, respond to criminal acts, or even to make predictions about future criminal activity.

One area that has received considerable recent attention is predictive policing. The technique accelerated in the United States after the National Institute of Justice started issuing grants for pilot predictive policing projects in At its core, these programs claim to predict with remarkable accuracy, based on massive data aggregation, where future crimes will be committed and which individuals are likely to commit those crimes.

Predictive policing has exploded in popularity. But there are growing concerns about algorithmic bias and prejudice, as well as the effectiveness of these predictions. PredPol then updates the algorithm each day with new events as they are received from the department. This may not represent fine-tuned algorithmic crime prediction as much as it involves the perpetuation of structurally biased policing.

China has enthusiastically embraced predictive policing as part of its Xinjiang crackdown. IJOP procures additional data from license plates and identification cards scanned at checkpoints, as well as health, banking, and legal records. Once IJOP flags an individual, that person is picked up by security forces and detained for questioning.

But there are still two missing things, which are required to implement to make the application extensible and configurable. First of all, all our video providers should be self-describable and self-configurable. Each class, which implements the IVideoSourceDescription interface, provides the name and description of the provider, which allows saving and loading its configuration and the creation of the configured video provider. Classes which implement the IVideoSourcePage interface represent a property page for the video provider configuration.

Combining all these together makes clear that a simplest module, which contains only one video provider, should contain three classes: provider description, provider configuration page, and the video provided itself.

And the last thing to make the solution working should be implemented on the application side — the application should find all modules and collect all information about the video source providers which live there. This actually can be done very easily through reflection. First of all, the application searches for all DLL files in the application folder.

Then, it tries to load each the file as an assembly and enumerate all types in the assembly, searching for types which implement the IVideoSourceDescription interface. This module's investigation procedure is called only once on the application startup, but the application can be easily modified to call the procedure by user request it may be useful if the user added a new video provider module, but does not want to restart the application.

There is one known bug of the. NET 1. Or to say it in a little bit different way — Microsoft was too picky, and implemented the first version of their framework very strictly conforming to the HTTP standard. Some cameras have something very little missing in the HTTP header and.

NET immediately refuses to work with them, generating a WebException with the following description:. Fortunately, it is a known feature of. NET and it is possible to fix it. First of all, you will need to get at least the 1. Then, you will need to create an application configuration file for your application, and place it in the application folder. The second problem is that the HttpWebRequest class of. NET has such a feature as connection group. By default, all HTTP requests are created in the same connection group, but each connection group has a limit of simultaneously opened connections.

So, this creates such a problem, that you cannot monitor many cameras at the same time. The good news is that the problem can also be solved easily — the HttpWebRequest class has a property called ConnectionGroupName , so you can manage connections grouping on your own. The attached application demonstrates all of the techniques described in the article, and allows monitoring of many different cameras from different manufacturers.

The application lets you monitor a single camera, or several cameras on a single screen at a time full screen mode supported. Other trademarks are trademarks of their respective owners. Surveillance Products:. Active WebCam webcam software, for surveillance and security systems, performs simultaneous recording and broadcasting from unlimited number of cameras. Currently, we have a team of five editors and seven writers to make sure that we not only put out well-researched pieces, but also make sure that all articles are easy to read and understand.

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