Sunday, October 18, 2020

QAnon: How a fringe internet phenomenon is now mainstream

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Vanshita Banuana

Article Title

QAnon: How a fringe internet phenomenon is now mainstream

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Global Views 360

Publication Date

October 18, 2020

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QAnon supporter in a Trump Rally

QAnon supporter in a Trump Rally | Source: Tony Webster via Wikimedia

In the age of the internet, conspiracy theories come a dime a dozen. They can be shared with an unimaginably huge audience with extreme ease. Most conspiracy theories center around specific large-scale events, but sometimes they do end up centering around a person instead. This has been recently observed in a group of conspiracy theorists called ‘QAnon,’ who are essentially supporters of incumbent U.S President Donald Trump, and believe that he is on a mission to expose a global secret network of high-profile pedophiles (and also cannibals, depending on who you ask).

QAnon followers believe that Democratic party members such as Hillary Clinton and Barack Obama are a part of this group, along with Hollywood celebrities such as Oprah Winfrey and Ellen DeGeneres. It is even believed that religious leaders like Pope Francis and the Dalai Lama are also in this group.

What is QAnon?

QAnon is an umbrella term for a large set of theories and sub-theories. It is considered a ‘big tent conspiracy theory,’ which means that it is still evolving and adding more claims under its belt. The most pervasive and foundational claim is that of a global cabal of pedophiles, and that Trump’s sole purpose is to unmask them.

It all started in October 2017, an anonymous account calling themself “Q Clearance Patriot” posted the first message associated with QAnon, on a site called 4chan. Q claimed to be a high ranking intelligence officer who knew classified information about Trump’s “war” against the aforementioned global cabal. Q also claimed to predict something called “The Storm,” which refers to the time Trump finally exposes the cabal and brings its members to justice.

The event’s title, “The Storm,” was inspired by a remark made during a photo op around the same time the first post appeared on 4chan. While standing with military generals (who QAnon followers believe recruited Trump to run for President with the aim of destroying the cabal) Trump made a remark about “the calm before the storm.” QAnon followers consider this to be a message for them. There have been many predictions about when this storm will occur, as well as other predictions that later never happened, such as Republicans winning a large number of seats in the 2018 midterm elections. As is common among conspiracy theorists, they twisted the results to continue to fit their beliefs.

The person(s) behind ‘Q,’ as the original poster is known, remains unknown. After first appearing on 4chan Q’s posts bounced around on similar sites. These days the posts— known as “drops”— are posted on a site called 8kun. To date, Q’s posts total to around 5,000, and there are some apparently popular apps that collect all past and present posts in one place. They are usually cryptic and use initials or codes to refer to people, such as HRC for Hillary Rodham Clinton, and POTUS (President of the United States) for Trump. QAnon followers use many common social media platforms like Twitter and Discord to discuss the meaning of the Q Drops.

Other QAnon claims include: Robert Mueller’s investigation into Russia’s collusion with the Trump campaign was actually a cover for investigating Clinton and Obama while Trump only pretended to be involved with Russia in order to force a third-party investigation; the cabal is involved in pedophilia and child murder either because they’re satanists or being blackmailed by the CIA (take your pick)

What was President Trump’s response?

President Trump (L) with Vice President Pence | Source: History in HD via Unsplash

Trump is idolised in QAnon theory, and what he says is monitored as closely as what Q says, and similar to Q’s drops, QAnon followers see messages and codes in things ranging from what number Trump says to what tie he wears, and decode the meaning of these perceived signals.

Anyone who knows anything about Trump knows he is incapable of denouncing anyone who supports him regardless of the absurdity of, or dangers posed by their actions. When asked about QAnon, Trump stated that while he didn’t know much about QAnon, he understood that they “like me very much.” The reporter explained Trump’s role in the conspiracy as a saviour from pedophiles and cannibals, to which Trump replied, “Is that a good thing or a bad thing?” He added that he hadn’t heard about that, but was “willing” to help “save the world from problems” if he can. On top of that, whether he knows or not, he has retweeted content from QAnon supporters multiple times.

Public figures are also revealing themselves to be QAnon followers, such as Marjorie Taylor Greene, a Republican candidate in Georgia who promoted QAnon— and she’s not the only one, joining a small-town mayor who supported QAnon during a radio broadcast. She was backed by Trump, who reportedly called Greene a future star, and called QAnon followers lovers of their country. Greene supposedly has a good chance of being elected to Congress.

Why is this becoming mainstream now?

A QAnon supporting sticker in Brooklyn, United States | Source: Robby Virus via Flickr

The QAnon member base is not a small one by any means. A singular QAnon on one social media platform like Facebook can reportedly have hundreds of thousands of members. It also seems that due to increased Internet usage during pandemic related lockdowns and work-from-home, more and more people are coming to know about QAnon, thereby increasing the number of people who believe and take part in it. There is, apparently, even a recently established church based on QAnon rhetoric that holds sessions via Zoom, and works to indoctrinate people into QAnon through tools such as videos and discussions.

In terms of group dynamics, QAnon has been compared to puzzle games due to the intricacy of the plot it weaves with the help of members’ contributions. Creating a shared reality, a common phenomena among conspiracy theorists, turns a political forum into a social environment, thereby deepening a person’s connection to a conspiracy via that people that they meet in these groups and other social media interactions with QAnon followers.

Perhaps due to the activity of coming together to decode Q’s drops, QAnon followers are intensely involved in the creation of the conspiracy itself, which makes this a unique kind of conspiracy theory, despite many elements of it being those often seen in various older conspiracy theories.

QAnon followers have been making waves offline as well, with a murder and a threat of a murder being attributed to QAnon followers. The FBI considers that QAnon poses a potential threat of domestic terrorism. Photos of Republican rallies in which signs of the letter Q and posters about QAnon are visible are becoming more and more common.

Additionally, QAnon followers seem to be making a joint effort to infiltrate anti-trafficking movements, both online as well as by attending rallies. Many members of QAnon believe that the global cabal is made up of child sex-traffickers or child-eating Satanists, thus making it easy for them to use campaigns such as #SaveTheChildren to lure or recruit people into their ideology. They have also been linked to spreading misinformation about COVID-19 and Black Lives Matter on social media sites such as Twitter and Facebook.

QAnon is a conspiracy theory that combines old and new elements, and which is already causing real harm to people and social causes. What truly makes matters worse, though, is that fact that the person at the center of the QAnon conspiracy, Donald Trump, is just as unlikely to see reason as QAnon followers themselves.

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February 4, 2021 5:22 PM

Automated Facial Recognition System of India and its Implications

On 28th of June 2019, the National Crime Records Bureau (NCRB) opened bids and invited Turnkey Solution providers to implement a centralized Automated Facial Recognition System, or AFRS, in India. As the name suggests, AFRS is a facial recognition system which was proposed by the Indian Ministry of Home Affairs, geared towards modernizing the police force and to identify and track criminals using Facial Recognition Technology, or FRT.

The aforementioned technology uses databases of photos collected from criminal records, CCTV cameras, newspapers and media, driver’s license and government identities to collect facial data of people. FRT then identifies the people and uses their biometrics to map facial features and geometry of the face. The software then creates a “facial signature” based on the information collected. A mathematical formula is associated with each facial signature and it is subsequently compared to a database of known faces.

This article explores the implications of implementing Automated Facial Recognition technology in India.

Facial recognition software has become widely popular in the past decade. Several countries have been trying to establish efficient Facial Recognition systems for tackling crime and assembling an efficient criminal tracking system. Although there are a few potential benefits of using the technology, those benefits seem to be insignificant when compared to the several concerns about privacy and safety of people that the technology poses.

Images of every person captured by CCTV cameras and other sources will be regarded as images of potential criminals and will be matched against the Crime and Criminal Tracking Networks and Systems database (CCTNS) by the FRT. This implies that all of us will be treated as potential criminals when we walk past a CCTV camera. As a consequence, the assumption of “innocent until proven guilty” will be turned on its head.

You wouldn’t be surprised to know that China has installed the largest centralized FRT system in the world. In China, data can be collected and analyzed from over 200 million CCTVs that the country owns. Additionally, there are 20 million specialized facial recognition cameras which continuously collect data for analysis. These systems are currently used by China to track and manipulate the behavior of ethnic Uyghur minorities in the camps set up in Xinjiang region. FRT was also used by China during democracy protests of Hong Kong to profile protestors to identify them. These steps raised concerns worldwide about putting an end to a person’s freedom of expression, right to privacy and basic dignity.

It is very likely that the same consequences will be faced by Indians if AFRS is established across the country.

There are several underlying concerns about implementing AFRS.

Firstly, this system has proven to be inefficient in several instances. In August 2018, Delhi police used a facial recognition system which was reported to have an accuracy rate of 2%. The FRT software used by the UK's Metropolitan Police returned more than a staggering 98% of false positives. Another instance was when American Civil Liberties Union (ACLU) used Amazon’s face recognition software known as “Rekognition” to compare the images of the legislative members of American Congress with a database of criminal mugshots. To Amazon’s embarrassment, the results included 28 incorrect matches.. Another significant evidence of inefficiency was the outcome of an experiment performed by McAfee.  Here is what they did. The researchers used an algorithm known as CycleGAN which is used for image translation. CycleGAN is a software expert at morphing photographs. One can use the software to change horses into zebras and paintings into photographs. McAfee used the software to misdirect the Facial recognition algorithm. The team used 1500 photos of two members and fed them into CycleGAN which morphed them into one another and kept feeding the resulting images into different facial recognition algorithms to check who it recognized. After generating hundreds of such images, CycleGAN eventually generated a fake image which looked like person ‘A’ to the naked eye but managed to trick the FRT into thinking that it was person ‘B’. Owing to the dissatisfactory results, researchers expressed their concern about the inefficiency of FRTs. In fact mere eye-makeup can fool the FRT into allowing a person on a no-flight list to board the flight. This trend of inefficiency in the technology was noticed worldwide.

Secondly, facial recognition systems use machine learning technology. It is concerning and uncomfortable to note that FRT has often reflected the biases deployed in the society. Consequently, leading to several facial mismatches. A study by MIT shows that FRT routinely misidentifies people of color, women and young people. While the error rate was 8.1% for men, it was 20.6% for women. The error for women of color was 34%. The error values in the “supervised study” in a laboratory setting for a sample population is itself simply unacceptable. In the abovementioned American Civil Liberties Union study, the false matches were disproportionately African American and people of color. In India, 55% of prisoners undertrial are either Dalits, Adivasis, or Muslims although the combined population of all three just amounts to 39% of the total population (2011 census). If AFRS is trained on these records, it would definitely deploy the same socially held prejudices against the minority communities. Therefore, displaying inaccurate matches. The tender issued by the Ministry of Home Affairs had no indication of eliminating these biases nor did it have any mention of human-verifiable results. Using a system embedded with societal bias to replace biased human judgement defeats claims of technological neutrality. Deploying FRT systems in law enforcement will be ineffective at best and disastrous at worst.

Thirdly, the concerns of invasion of privacy and mass surveillance hasn’t been addressed satisfactorily. Facial Recognition makes data protection almost impossible as publicly available information is collected but they are analyzed to a point of intimacy. India does not have a well established data protection law given that “Personal data Protection Bill” is yet to be enforced. Implementing AFRS in the absence of a safeguard is a potential threat to our personal data. Moreover, police and other law enforcement agencies will have a great degree of discretion over our data which can lead to a mission creep. To add on to the list of privacy concerns, the bidder of AFRS will be largely responsible for maintaining confidentiality and integrity of data which will be stored apart from the established ISO standard. Additionally, the tender has no preference to “Make in India'' and shows absolutely no objections to foreign bidders and even to those having their headquarters in China, the hub of data breach .The is no governing system or legal limitations and restrictions to the technology. There is no legal standard set to ensure proportional use and protection to those who non-consensually interact with the system. Furthermore, the tender does not mention the definition of a “criminal”. Is a person considered a criminal when a charge sheet is filed against them? Or is it when the person is arrested? Or is it an individual convicted by the Court? Or is it any person who is a suspect? Since the word “criminal” isn’t definitely defined in the tender, the law enforcement agencies will ultimately be able to track a larger number of people than required.

The notion that AFRS will lead to greater efficacy must be critically questioned. San Francisco imposed a total ban on police use of facial recognition in May, 2019. Police departments in London are pressurized to put a stop to the use of FRT after several instances of discrimination and inefficiency. It would do well to India to learn from the mistakes of other countries rather than committing the same.

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