Internet censorship is a phenomenon of societal importance and attracts investigation from multiple disciplines. Several research groups, such as Censored Planet, have deployed large scale Internet measurement platforms to collect network reachability data. However, existing studies generally rely on manually designed rules (i.e., using censorship fingerprints) to detect network-based Internet censorship from the data. While this rule-based approach yields a high true positive detection rate, it suffers from several challenges: it requires human expertise, is laborious, and cannot detect any censorship not captured by the rules. Seeking to overcome these challenges, we design and evaluate a classification model based on latent feature representation learning and an image-based classification model to detect network-based Internet censorship. To infer latent feature representations fromnetwork reachability data, we propose a sequence-to-sequence autoencoder to capture the structure and the order of data elements in the data. To estimate the probability of censorship events from the inferred latent features, we rely on a densely connected multi-layer neural network model. Our image-based classification model encodes a network reachability data record as a gray-scale image and classifies the image as censored or not using a dense convolutional neural network. We compare and evaluate both approaches using data sets from Censored Planet via a hold-out evaluation. Both classification models are capable of detecting network-based Internet censorship as we were able to identify instances of censorship not detected by the known fingerprints. Latent feature representations likely encode more nuances in the data since the latent feature learning approach discovers a greater quantity, and a more diverse set, of new censorship instances.

Analysts have built an artificial intelligence or AI technology that can naturally learn and adjust to dodge internet censorship, a development that might open up hindered online substance for a huge number of individuals living in India and China. The scientists, including those from the University of Maryland (UMD) in the US, tried the AI technology in China, India, and Kazakhstan, and observed many ways of conquering oversight by taking advantage of gaps in rationale utilized by controls and finding bugs that are hard for people to find physically. The specialists said they intend to present the AI technology called Geneva – – short for Genetic Evasion – – during a friend looked into talk at the Association for Computing Machinery's 26th Conference on Computer and Communications Security in the UK. Geneva addresses the initial move toward a totally different weapons contest in which artificial intelligence frameworks of controls and dodgers contend with each other. At last, dominating this race implies carrying free discourse and open correspondence to a large number of clients all over the planet who as of now don't have them.

Countries like China, Iran and Russia are known for strictly censoring what their citizens can see on the internet. These authoritarian governments do this to control their people and protect those in power. It can be very difficult, and often dangerous, to try to get around this, but a new tool looks like it could be the best way to beat censorship in these kinds of oppressive countries. Researchers at the University of Maryland have developed a kind of AI that they've named Geneva, which stands for "Genetic Evasion." This AI uses a kind of machine learning to automatically detect bugs and gaps in a country's censorship system so the user can view uncensored content.

Internet censorship, basically, is a very effective strategy used by dictatorial governments to limit access to information available online for controlling freedom of expression and prevent rebellion and discord. Countries at the forefront of adopting Internet censorship, as per the findings of the 2019 Freedom House report, are India and China as these are declared to be the worst abusers of digital freedom. Conversely, the US, Brazil, Sudan, and Kazakhstan are the countries where Internet freedom has considerably declined recently. When a country curbs Internet freedom, activists need to find ways to evade it. However, they may not need to manually search for it now that "Geneva" is here. The term is a shorter version of Genetic Evasion.

Internet censorship by authoritarian governments prohibits free and open access to information for millions of people around the world. Attempts to evade such censorship have turned into a continually escalating race to keep up with ever-changing, increasingly sophisticated internet censorship. Censoring regimes have had the advantage in that race, because researchers must manually search for ways to circumvent censorship, a process that takes considerable time. New work led by University of Maryland computer scientists could shift the balance of the censorship race. The researchers developed a tool called Geneva (short for Genetic Evasion), which automatically learns how to circumvent censorship.

Internet censorship by authoritarian governments prohibits free and open access to information for millions of people around the world. Attempts to evade such censorship have turned into a continually escalating race to keep up with ever-changing, increasingly sophisticated internet censorship. Censoring regimes have had the advantage in that race, because researchers must manually search for ways to circumvent censorship, a process that takes considerable time. New work led by University of Maryland computer scientists could shift the balance of the censorship race. The researchers developed a tool called Geneva (short for Genetic Evasion), which automatically learns how to circumvent censorship.

New work led by University of Maryland computer scientists could shift the balance of the censorship race. The researchers developed a tool called Geneva (short for Genetic Evasion), which automatically learns how to circumvent censorship. Tested in China, India and Kazakhstan, Geneva found dozens of ways to circumvent censorship by exploiting gaps in censors' logic and finding bugs that the researchers say would have been virtually impossible for humans to find manually. The researchers will introduce Geneva during a peer-reviewed talk at the Association for Computing Machinery's 26th Conference on Computer and Communications Security in London on November 14, 2019. "With Geneva, we are, for the first time, at a major advantage in the censorship arms race," said Dave Levin, an assistant professor of computer science at UMD and senior author of the paper.

Google's first foray into Chinese markets was a short-lived experiment. Google China's search engine was launched in 2006 and abruptly pulled from mainland China in 2010 amid a major hack of the company and disputes over censorship of search results. But in August 2018, the investigative journalism website The Intercept reported that the company was working on a secret prototype of a new, censored Chinese search engine, called Project Dragonfly. Amid a furor from human rights activists and some Google employees, US Vice President Mike Pence called on the company to kill Dragonfly, saying it would "strengthen Communist Party censorship and compromise the privacy of Chinese customers." In mid-December, The Intercept reported that Google had suspended its development efforts in response to complaints from the company's own privacy team, who learned about the project from the investigative website's reporting. Observers talk as if the decision about whether to reenter the world's largest market is up to Google: will it compromise its principles and censor search the way China wants?