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Just how easy is it to opt out of corporate data collection? Are you just a luddite if you do choose to opt out? And what are the costs?

Speaking Tuesday at the Princeton University's Center for Information Technology Policy was Janet Vertesi(@cyberlyra), who specializes in the sociology of science, knowledge, and technology. Janet has spent the past seven years studying several NASA spacecraft teams as an ethnographer. At Princeton, Janet teaches classes on the Sociology of Technology; Sociology of Science; Work, Technology and Organizations; and Human-Computer Interaction.

The basic assumption of markets is that people have choices. This idea that "you can just vote with your feet" is called an "exit option democracy" in organizational sociology ( Weeks, 2004). Opt-out democracy is not really much of a democracy, says Janet. She should know-she's been opting out of tech products for years.

Today, Janet tells us about four personal experiments to opt out of technology companies: (a) platform avoidance, (b) infrastructural avoidance (c) hardware experiments, and (d) the idea of digital homesteading.

Janet reminds us that she doesn't hate tech companies. She first encountered computers as a six year old, and she was a BBS operator for her high school, and she publishes computer science research, some of which she did while at Yahoo Research. Janet's perspective on technology is shaped by her work on science and technology studies, which looks at how technologies come about and the invisible values they bring with them.

As a scholar, Janet uses methods of " infrastructural inversion" to reveal the invisible, sunken infrastructures and their categorical assumptions, bringing them into view for analysis (Bowker 1994, Bowker and Star 1999). Janet also uses methods of Critical Technical Practice, which deploy critical theory, analysis, and reflection on the unconscious assumptions in the process of design and building alternative systems (Agre, 1992; Sengers et al 2005). Throughout this work, when asking questions about the values of technology, Janet tries to ask, "Could it have been otherwise" (Pinch & Bijker 1984).

Janet describes her opt-out experiments as infrastructural inversions that show how hard it is for anyone to opt out.

Janet Vertesi describes the values behind her opt-out project

What does it take to get off a platform? In 2012, Google decided to aggregate and correlate data across their platform sites, including YouTube, Maps, Mail. They used this data for pigeonholing users for the purposes of advertising sales ("a better user experience").

As a sociologist, Janet was incensed. First, we know from sociology that people need to have the right to behave differently in different settings (fundamental to Goffman and 60 years of sociology). For Google to create a single identity was also risky. At the time, Arvind Narayanan, Ed Felten, and others were showing that combining datasets like this could de-anonymize people's identities.

At the time, Janet was in a long term relationship with Google. She was one of the first people to sign up for gmail. The company knew she was engaged before her colleagues did. All of her coordinating was on Google, all of her relationships were on Google.

How do you break up with a tech company? The first step is to get your stuff back. So Janet downloaded all her data. Just like someone in a relationship, the service tried to emotionally manipulate her to stay: "Are you sure you want to do this?" "Our servers are feeling unloved!" It was all emotional blackmail. Janet recently went off Facebook, and they use the same kind of emotional messages- breaking up is hard to do.

What can you do once you've broken up? Janet needed to find ways to stay out of the relationship.

To prevent Google from accessing her data, Janet practices "data balkanization," spreading her traces across multiple systems. She's used DuckDuckGo, sandstorm.io, ResilioSync, and youtube-dl to access key services. She's used other services occasionally and non-exclusively, and varied it with open source alternatives like etherpad and open street map. It's also important to pay attention to who is talking to whom and sharing data with whom. Data balkanization relies on knowing what companies hate each other and who's about to get in bed with whom.

Janet also adjusted her life for living without social network software. She created a list of her friends, made sure to call her friends, made a list of hobbies, and sent a calendar reminder to her friends with her birthday.

Finally, Janet decided the minimum systems she needed and limited her use just to those things. For example, Janet uses Twitter to connect to astronomers, and uses it only for that.

Overall, going Google free was was straightforward, says Janet. You can create a program and stick to it. But platforms will still have your data, and many things become harder, including relationships family and friends. You will probably be blamed, again and again, for social inconveniences.

Janet Vertesi describes techniques for avoiding data sharing by stores and websites

What does it take to avoid the personal data dragnet? Now that tech companies share more and more data with each other, we rarely have the choice to opt out.

Janet decided to prevent the internet from knowing about her children

Around the time Janet became pregnant with her first child, she learned that online and offline purchases and browsing related data were being combined to facilitate targeted marketing. Pregnant women are especially vulnerable- if companies can identify pregnant women early, they can be the first to influence long-term purchasing decisions. So Janet decided to prevent the internet from knowing about her children.

How did Janet try to prevent companies from knowing about her two children? The first step was to browse the internet using Tor, making it harder for companies to track her browsing behavior. Janet and her husband also used cash, gift cards, and amazon gift cards to make purchases, linking them with Amazon lockers that were disconnected from their identities.

Trying to de-link your identity from data storage has consequences. For example, when Janet and her husband tried to use cash for their purchases, they faced risks of being reported to the authorities for fraud, even though their actions were legal.

Social capital and network effects keep people conscripted online

How can you check that your attempts to stay out of the data infrastructure are working? To find out, Janet's family created inversion techniques to monitor corporate data collection through the ads they received in the mail.

First, they created special email addresses linked to a series of mailing addresses, with purpose specific credit card numbers from privacy.com, and released those emails only to certain sources. They received only three baby product catalogs during this experiment. In one case, a friend had added their child to an online service when sending an invitation. In another case, an Australian company that sold parenting products moved to San Francisco and started selling their data.

Opting out of data infrastructure for five years has involved many social consequences. One day, a family member sent a private message on Facebook congratulating her for her upcoming child. Janet immediately deleted all the messages and unfriended the family member. The family members didn't realize that Facebook reads private messages. Janet has also lost substantial social capital from friendship networks by not exchanging baby news. Janet says that her experiences reveal how social capital and network effects keep people conscripted online.

How easy is it to opt out from data sharing? Several friends have tried to do this project, and no one has succeeded more than 4 or 5 months.

Janet tells us that phones are also a leaky source of personal information and data. Phone numbers and other identifiers allow companies and governments to trace calls and locations. Apps and OSes require access to our data. And only a small number of companies control all of this data.

The obvious answer is to build your own smartphone, so Janet used parts from electronics kits to make her own 2g phone. After making the phone Janet quickly realized even a privacy-protecting phone can't connect to the network without identifying the user to companies through the network itself.

Since then, Janet has experimented with phones and OSes that offer more security features or that are just no longer monetized by companies. For example, Janet started to use Nokia MeeGo after the project became defunct. She has also used the Sailfish OS on the Sony Experia X, which is an open hardware design.

Even a privacy-protecting phone can't connect to the network without identifying the user to companies through the network itself

When working with open hardware phones, Janet has used a range of inversion techniques to reveal the nature of data tracking by trying to avoid it:

avoid the two major mobile phone makers

install apps via open OSes, using command line installation or no app store

use obsolete devices that companies are no longer monitoring and monetizing

accept lower grade networks for more options (2G, 3G, non-LTE)

keep duplicates and triplicates because old phones break

avoiding the IMEI/SIM problem is hard. Janet is thinking about launching her own satellite

Opting out in this way also has social consequences. Sometimes the network doesn't work well, and people can't always reach you. You also have less access to apps. Degraded service requires adjusting expectations. Janet tells us that no luddite could ever op out; only a dedicated hobbyist ever could do this.

Is it possible to have a smart home without giving companies direct access to your home? To ask this question, Janet has turned to the history of homesteading to think about a life where people create their own digital infrastructures, independently from companies.

The life of a digital homesteader is in a continuous state of disrepair

What is life on the digital homestead like? Janet's family has their own media library, their own calendar on a server they run, and a baby cam that they independently maintain. All of this infrastructure is served on their own home network, served from a Raspberry Pi that they built. Janet tells us about the techniques for digital homesteading:

keep personal data on hardware or services you own or control

purchase hardware that can be re-appropriated, programmed, and locally controlled

deploy open source equivalents

de-consolidate user accounts, and use them to track where your data is going

run your own DNS

dual or multi-boot computers to access a range of software as needed

The life of a digital homesteader is in a continuous state of disrepair. If you're a tinkerer, this is a lot of fun. Unfortunately for essential services, it can also be dangerous. Janet's family also lost a lot of information security through digital homesteading.

Janet says that these experiments worked because they reveal how companies use data and infrastructure and because they show how hard it is to opt out. As Janet found, opting out introduces constant trade-offs, showing that exit-option democracy is not only not an option, but not democracy.

Janet concludes with further thoughts on the implications for research and design:

Security through scale versus security by obscurity is a terrible trade-it leaves out data ownership concerns

Studies of Internet of Things, platforms, and online systems assume we use corporate infrastructures and not DIY options.

We need more attention to individualized security and system choice within heterogenous networks in the context of constant repair- what Janet calls "in the box" security

Privacy is not just a set of "who can see what" controls: can we permit users to dictate where and how their data is stored and accessed, not just by users but by machines, algorithms and companies?

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Quitting Facebook & Google: Why Exit Option Democracy is the Worst Kind of Democracy

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Just how easy is it to opt out of corporate data collection? Are you just a luddite if you do choose to opt out? And what are the costs? Speaking Tuesday at the Princeton University's Center for Information Technology Policy was Janet Vertesi(@cyberlyra), who specializes in the sociology of science, knowledge, and technology.

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https://drive.google.com/file/d/1hFvB_cGM_TfVVNuXV-Jg6mO4DIPLvNoC/view

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Practical Internet Privacy
by Jessamyn West
librarian.net/talks/privacy

Slides and Notes

Privacy Links

Threat model

EFF's Intro to Threat Modeling:

https://ssd.eff.org/en/module/introduction-threat-modeling

FERPA and protecting student privacy from US Department of Education

https://studentprivacy.ed.gov/

Privacy threats in intimate relationships

https://academic.oup.com/cybersecurity/article/6/1/tyaa006/5849222

Passwords

XKCD on Password Strength

https://xkcd.com/936/

Password managers

http://thewirecutter.com/reviews/best-password-managers/

Everything You Know About 'Secure' Passwords Is Wrong

https://www.tomsguide.com/us/password-advice-wrong,news-25617.html

Two Factor Authentication

https://twofactorauth.org/

Internet Traffic Listening/Recording Tracking Trust but Verify - Learn More Further reading Presented in...
April 11, 2017 (Randolph VT)
May 18, 2017 (Killington VT)
June 7, 2017 (Rockingham VT)
October 23, 2017 (Burlington VT)
February 11, 2018 (Starksboro VT)
May 7, 2018 (Brattleboro VT)
June 6, 2018 (Richmond VT)
September 17, 2018 (Enfield NH)
September 24, 2018 (Charlotte VT)
October 25, 2018 (Hinesburg VT)
November 8, 2018 (TX State Library webinar)
April 12, 2019 (WILS Webinar)
October 3, 2019 (OPLIN Webinar)
March 5, 2020 (Hartland, VT)
May 7, 2020 (Peacham, VT)
July 16, 2020 (SEFLIN Webinar)

Huge thanks to TJ for the starter version of this list and Graham John Pegg for the info icon

CC BY-SA 4.0.

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Duration: 11 months, from October 1st 2019 to August 2020Location: Downtown Toronto and ScarboroughApplication Deadline: September 5, 2019 Trinity Square Video and the Digital Justice Lab are announcing an open...

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The Consentful Tech Project - The Consentful Tech Project raises awareness, develops strategies, and shares skills to help people build and use technology consentfully.

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We're gearing up to offer an intensive 6-8 week UX/UI design training! It will be based in popular education, design justice, and consentful tech, and will prioritize participants who are...

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DuckDuckGo is a search engine that offers several unique and useful features for web searchers, such as streamlined shortcuts and instant answers.

Below are 10 different things you might not know you could accomplish with the DuckDuckGo search engine and you can see how it differs from competitors like Google.

DuckDuckGo is a search engine that delivers effective, fast, relevant results, and is especially attractive if you're keeping track of how information is gathered about you online.

DuckDuckGo offers a few features that are worth a second look for the savvy web searcher:

The DuckDuckGo results pages aren't paginated, making it easy to scroll down and find what you're looking for quickly.

Favicons (the small images that show up in the address bar, unique to each site) are displayed next to search results for instant recognition of your favorite sites.

Instant answers called "zero-click info" show up before any other result, providing the answer to your question right upfront.

DuckDuckGo gives users the ability to jump to a website directly using bangs, which are shortcuts you can enter into DuckDuckGo that bypass the search results. There are thousands of DuckDuckGo bang shortcuts, covering a wide multitude of sites varying in topics like research, tech, entertainment, and news. Here's one example you could enter into the search engine to jump to Amazon.com: !a 55" tv.

In addition to the shortcuts given above, DuckDuckGo offers what they call goodies, an intriguing array of all kinds of search shortcuts, anything from special keyboard shortcuts to specialized cheat sheets.

"DuckDuckGo prevents search leakage by default. Instead, when you click on a link on our site, we route (redirect) that request in such a way so that it does not send your search terms to other sites. The other sites will still know that you visited them, but they will not know what search you entered beforehand . . . DuckDuckGo takes the approach to not collect any personal information. The decisions of whether and how to comply with law enforcement requests, whether and how to anonymize data, and how to best protect your information from hackers are out of our hands. Your search history is safe with us because it cannot be tied to you in any way."

Protecting web privacy is becoming more of an issue for many people as the internet continues to evolve. If you're concerned with privacy and you enjoy a simple, uncluttered interface with plenty of shortcuts, then DuckDuckGo would probably be a good choice for you as a search engine.

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10 Things That Might Surprise You About DuckDuckGo

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DuckDuckGo is a search engine that offers several unique and useful features for web searchers, such as streamlined shortcuts and instant answers. Below are 10 different things you might not know you could accomplish with the DuckDuckGo search engine and you can see how it differs from competitors like Google.

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ABOUT US

We believe everyone should be able to explore the internet with privacy. We are the Tor Project, a 501(c)(3) US nonprofit. We advance human rights and defend your privacy online through free software and open networks. Meet our team.

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The Tor Project | Privacy & Freedom Online

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Anonymity Online | Defend yourself against tracking and surveillance. Circumvent censorship.

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It's a no from me ...

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https://anthology.rhizome.org/how-do-you-see-the-disappeared-a-warm-database

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Are.na is a platform for connecting ideas and building knowledge.

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1. Twitter Finally Cracked Down on QAnon-but There's a Catch

2. Topless Beach Drone Scandal!

3. Customs and Border Protection Can Track Cars Nationwide Via Commercial Database

4. Pay Every American $1,000 to Download a Contact Tracing App

Even if you aren't interested in artificial intelligence, A.I. is interested in you. When you upload and tag a photo of yourself on Facebook, you help improve the facial recognition software the company uses to link faces to identities. Your photo might also end up with companies like IBM and Microsoft, which crawl the web for public images--from selfies with your friends to celebrities on the red carpet--to build facial recognition systems that they then market to law enforcement, border control, and private security companies. These systems, which are racially biased and unregulated, may be used on you by the police when you're walking down the street. And now, Customs and Border Protection contracts companies like SITA to implement facial recognition for passengers boarding international flights, alongside similar plans from the Transportation Security Administration to roll out face scanners for domestic flights. The constant surveillance to which internet companies have acclimated their users is slowly making its way offline, where it becomes increasingly difficult to opt out of these systems.

This has multiple legal implications, as the ethical basis for so many of these data collection systems is that users have consented to the way companies often manipulate or sell their data. In an age where we TOS;DR -terms of service, didn't read-clicking the "accept" button often gives companies carte blanche to use your private data, and it's frequently impossible to know where your data will go. Like the tobacco companies that preceded them, companies like Amazon and Facebook can argue that you've made an informed choice: You consented to their terms of service and willingly uploaded the photos, so you can't blame them for the results.

To protect consumers' data online, the European Union and the state of California have begun to adopt regulations that strengthen a user's right to be informed about and intervene in the processing of their personal data. Both the EU's General Data Protection Regulation and the California Consumer Privacy Act require companies to ask for consent prior to data collection processes and give users the right to know what data will be used for what purpose. The proposed Commercial Facial Recognition Privacy Act introduced in the Senate has similar consent requirements for facial recognition data. Philosophers Luciano Floridi and Mariarosaria Taddeo-directors of Oxford's Digital Ethics Lab, which collaborates with Google, Microsoft, and the European Commission to develop ethical standards-have also highlighted consent as a major component of an ethical framework for artificial intelligence. Consent, in other words, has become one of the major legal and ethical benchmarks for regulating data collection processes.

But consent isn't an ethical rubber stamp. If anything, it allows those in power to avoid responsibility for its consequences by shifting the ethical responsibility to safeguard personal information away from the companies onto individual consumers. Many companies have chosen to comply with the GDPR by blocking all EU users; others bury users in a deluge of pop-ups that have large "accept" buttons but hidden opt-out options. These tactics provide the illusion of choice while suppressing actual alternatives-a phenomenon that also exists offline. Yet relying on consent for ethical reasoning about surveillance is so pervasive that people publish articles on how to jump through increasingly complicated hoops to opt out of airport face scanning instead of questioning why we never had a choice about whether the scanners were installed.

More importantly, opting out of data collection systems often means not being able to use online services. Refusing to participate in these systems often means staying off the internet entirely, which comes with massive social and economic costs. The question therefore isn't whether you should opt out; it's a question of whether you can afford to.

What would an algorithm's design look like if it were subject to public hearings and approval prior todeployment?

Opting out in the offline world is just as difficult, if not more. Good luck taking a walk through the park without getting caught on camera, where your only opt-out option might be to wear a Hunger Games-style getup to fool face detection algorithms. If you have a driver's license or state ID, you might be one of at least half of all U.S. adults who are included in government facial recognition databases with no recourse. And at the airport, staff do not disclose the ability to opt out of face scanning. But refusing the biometric scans at airports often means that you face detainment and invasive searches.

Facial recognition technologies are already being integrated into policing practices that disproportionately target communities of color. As such, people of color might reasonably choose to opt out of training the system that will be used to surveil them with greater precision. Hypothetically, if enough people do this, then the training data sets will again skew toward lighter-skinned people. This leaves communities of color in a double-bind: Opt out, and the racial bias of a data set increases, leading to more soap dispensers that simply don't respond to dark skin. Opt in, and have your image be used to fine-tune a facial recognition system that Amazon is trying to sell to Immigration and Customs Enforcement.

Even if you don't consent to having your data used, companies and governments will simply turn to more vulnerable faces to populate their data sets. The very narrow choice to withdraw consent does not allow an individual to reject unjust uses of technology that will affect not only themselves, but their wider communities. A.I. for facial recognition surfaces racism that is already embedded within American surveillance and policing practices. Collecting more data or making users more informed about how their data is being used won't fix that.

We aren't saying that consent has no place in this ecosystem. But it shouldn't be the only way we let people make decisions about data protection. Researchers like Sasha Costanza-Chock and nonprofit organizations like the Participatory Budgeting Project have also looked toward participatory approaches to data set creation, research, and analysis that involve community input at the outset, rather than seeking consent for opaque decisions after the fact. For example, the Make the Breast Pump Not Suck Hackathon at MIT attempts to rethink the design process by pairing technologists with community advocates in birth clinics, in-home maternal care programs, and peer counseling/support groups to collaboratively design new technologies from scratch.

Yes, it's impossible to ensure that all voices are heard and accounted for-the final product will always be a series of compromises between different community stakeholders. But as the system stands, there are few-if any-participatory approaches to the creation and design of these algorithms. While these kinds of granular, in-depth collaborations are easier to achieve on a local level, it's possible to imagine a world where this kind of public accountability and engagement scales up: What would an algorithm's design look like if it were subject to public hearings and approval, like alcohol licenses or building permits, prior to deployment?

There is power in numbers. To A.I., any individual's data is interchangeable. But linked with other photos in a data set, its capabilities are amplified. By framing data protection as an individual choice, companies hope to prevent us from collectively reflecting on how our information is used in society. In Somerville, Massachusetts, City Councilor Ben Ewen-Campen spoke against the common notion that privacy invasions are inevitable with technological change. "The community, activists, the government working together can actually shape this stuff," said Ewen-Campen. "We don't have to just sit back and take it."

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society.

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Consent Won't Magically Fix Our Data Privacy Problems

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Twitter Finally Cracked Down on QAnon-but There's a Catch Topless Beach Drone Scandal! Customs and Border Protection Can Track Cars Nationwide Via Commercial Database Pay Every American $1,000 to Download a Contact Tracing App Even if you aren't interested in artificial intelligence, A.I. is interested in you.

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Potential class action lawsuits have been filed against IBM and Clearview AI for alleged violations of the Illinois' Biometric Information Privacy Act (BIPA), as strikingly different projects are drawn into similar entanglements with privacy regulation.

The suit against IBM, shared by Bloomberg Law, alleges that subjects appearing in the company's Diversity in Faces (DiF) Dataset who reside in Illinois have had their informed consent rights under BIPA violated by the company, which collected images from Flickr and then annotated them. The suit is filed on behalf of all Flickr users in Illinois whose photos are found in the dataset, and who did not consent to the biometric data analysis and storage.

When the DiF Dataset was launched, IBM Fellow and Manager of AI Tech Dr. John Smith told Biometric Update that it represented an important step towards improving the fairness and accuracy of facial recognition algorithms.

After concerns were aired about the source of images used in the dataset, IBM said that it would cooperate with requests to remove URLs from the images.

The case has been filed in Cook County Court.

Just days after being ordered to delete data and cease its image-collection practices by Twitter, Clearview AI is facing a BIPA lawsuit which may be only the tip of the iceberg, for collecting the facial biometrics of Illinois residents without consent, MediaPost reports.

The company has been selling its database to police departments around the U.S., according to the New York Times.

"Without obtaining any consent and without notice, Defendant Clearview used the internet to covertly gather information on millions of American citizens, collecting approximately three billion pictures of them, without any reason to suspect any of them of having done anything wrong, ever," plaintiff David Mutnick of Illinois alleges in the complaint, which was filed in federal court. "After obtaining these images, Clearview used artificial intelligence algorithms to scan the facial geometry of each individual depicted in the images, a technique that violates multiple privacy laws."

In addition to damages the plaintiff is seeking a court order to have the distribution of the data stopped, and the images deleted.

Attorney Jay Edelson, who does not represent Mutnick but has brought cases against tech companies previously, also expects to file suit against Clearview AI, according to the report. Edelson also intends to seek a court order closing down the company.

"I do not believe that what they're doing is proper, and we feel very good about being able to explain that to a court," he told MediaPost.

Defense strategies against BIPA suits are still evolving, but the Supreme Court recently made clear in a decision against Facebook that federal courts will recognize the legal standing criteria established by the state's legislation.

Article Topics

biometric data | biometrics | BIPA | Clearview AI | data protection | data storage | dataset | facial recognition | IBM | lawsuit | legislation | privacy | United States

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Biometric privacy suits leveled against IBM and Clearview AI in Illinois

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Potential class action lawsuits have been filed against IBM and Clearview AI for alleged violations of the Illinois' Biometric Information Privacy Act (BIPA), as strikingly different projects are drawn into similar entanglements with privacy regulation.

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Send a Message

Immigration and Customs Enforcement is continuing deportations and raids throughout the country during the coronavirus pandemic, despite lockdowns in multiple cities and official mandates for people to avoid interaction.
ICE agents are arresting people and detaining them in unsanitary and enclosed detention centers where it will be almost impossible to avoid infection.
Tech companies are currently enabling these raids despite the national public health crisis, providing the infrastructure ICE agents use to find targets, conduct raids, and make arrests.
We're calling on every tech company that works with ICE to immediately halt its support for the agency at this time. The hundreds of contractors on which ICE relies can still put a halt to its operations.
Silicon Valley can take a stand. Stop working for ICE immediately.

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Immigration and Customs Enforcement is continuing deportations and raids throughout the country during the coronavirus pandemic, despite lockdowns in multiple cities and official mandates for people to avoid interaction. ICE agents are arresting people and detaining them in unsanitary and enclosed detention centers where it will be almost impossible to avoid infection.

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how do we move from individual refusal to collective refusal? individual opt out is often used to shift responsibility from tech firms onto users

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As some inspiration our amazing Feminist Data Workshop collaborator Anna Lauren Hoffmann created this awesome Tumblr on refusal: https://on-refusal.tumblr.com/

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As some inspiration our amazing Feminist Data Workshop collaborator Anna Lauren Hoffmann created this awesome Tumblr on refusal: https://on-refusal.tumblr.com/

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NEW CV Dazzle Looks N°6, N°7. Developed for Designs for a Different Future 2020 to break convolutional neural network face recognition. © Adam Harvey 2020.

CV Dazzle is a form of camouflage from computer vision created in 2010 as my masters thesis at New York University's Interactive Telecommunications Program. Unlike traditional camouflage, such as disruptive-pattern material, that hides the wearer from human observation, CV Dazzle is designed to break machine vision systems while still remaining perceptible to human observers. It is the first documented camouflage technique to successfully attack a computer vision algorithm.

In this proof of concept research project from 2010 the technique was used to break the widely-used (at the time) Viola-Jones face detection algorithm by using bold patterning to break apart the expected features of the face detection profiles (haarcascades). This algorithm gradually become deprecated in security around 2013-2016 and is no longer used, therefore the original patterns (designed between 2010-2013) are no longer active looks. But until then it was the only face detection algorithm and therefore created a single point of failure when broken, cascading throughout the security industry. All of sudden a key technology in the previously infallible post 9/11 security apparatus could be foiled by makeup and hairstyles.

CV Dazzle Look 5. Commission for the New York Times Op-Art. 2013. © Adam Harvey 2013.

This page is an overview of the project concepts, motivation, and initial tests. More technical information about reverse engineering and visualizing the vulnerabilities in the haarcascade and other computer vision algorithms may be published here in the future.
Updates

March 1, 2023: cvdazzle.com now redirects to this page where I maintain update and accurate information about the project. Tested using new 3D rendering workflow.

September 2020: Two new 3D-designed CV Dazzle looks presented at Designs for Different Future Walker Center of Art (scroll down to related media)

How It Works #

The initial CV Dazzle designs work by altering the expected dark and light areas of a face (or object) according to the vulnerabilities of a specific computer vision algorithm. In the image above (Look #5), the design targets the Viola-Jones face detection algorithm, a popular (at the time of development) and open source face detector that is included with the OpenCV computer vision framework. But CV Dazzle is not a specific design or pattern. CV Dazzle is a camouflage strategy to evade computational vision systems that evolves, as camouflage does, alongside the technology it aims to subvert. Patterns and designs are always specific to the wearer, algorithm, and environmental conditions.

Designs can be created using only hair styling, makeup, and fashion accessories, which could be customized to any wearer's style and are low-cost or free, and accessible to a wide audience. Newer forms of a CV Dazzle approach could target other algorithms, such as deep convolutional neural networks, but would require finding vulnerabilities in these algorithms. Because computer vision is a probabilistic determination, finding the right look is about finding how to appear one step below the threshold of detection.

To guide the development of the initial looks for the project, a genetic algorithm was used to find the optimal faces hidden within the algorithm. For over a decade, since the development of the Viola-Jones algorithm in 2003, these were the prototypical face appearances used to detect the human face in security videos. The key observations are the heavy reliance on the dark areas around the eyes, the symmetry, the stability of the nosebridge, and the darkness under the nose. By using makeup and hairstyling the dark and light areas can be reversed to lower the probability of detection through the various stages of the haarcascade profile, a multi-stage detector that uses around 20-25 stages of scoring during the detection process. A visualization of process is shown in this research video.

Haarcascade frontalface reverse face

Haarcascade frontalface-alt-tree reverse face

Haarcascade frontalface-alt reverse face

Haarcascade frontalface-alt2 reverse face

Since face detection is the first step in any automated facial recognition system, blocking the detection stage also blocks any subsequent facial analysis including recognition and emotional analysis. Therefore, CV Dazzle could be used to block facial recognition by blocking face detection using only hair styling and makeup. This is the strategy used for the looks on this page, which all target the Viola-Jones haarcascade face detection algorithm. If a face is not detected by the algorithm, it effectively blocks the subsequent recognition algorithms. Often these two algorithms are conflated, but they are entirely different, each with its own set of vulnerabilities.

Many community and activist uses of CV Dazzle have targeted face recognition, instead of detection. This is also a valid approach just not the approach used for the proof-of-concept. Face recognition, like detection, merely computes a probability for how much one face looks like others, typically using a cosine similarity metric with a 512, 1024, or 2048 length feature vector describing the face. Adding facial coverings, makeup, or prosthetic will affect this score to varying degrees.

The first design (Look #1) was created in 2010 as a proof of concept for my masters thesis at ITP NYU. The subsequent 5 looks were created in collaboration with DIS Magazine (Looks 2-4) and for a commission for the New York Times (Looks 5-6). The goal of of these test looks was to investigate the potential for a style that was functional but still within the margins of conceptual fashion.

CV Dazzle Look 1. 2010. Hair by Pia Vivas Model: Jen Jaffe Original look for CV Dazzle thesis at NYU. Photo: ©Adam Harvey

CV Dazzle Look 2. For DIS Magazine Creative direction by Lauren Boyle and Marco Roso. Model: Irina. 2010. Hair by Pia Vivas. Photo: ©Adam Harvey

CV Dazzle Look 3. For DIS Magazine. Creative direction by Lauren Boyle and Marco Roso. Model: Jude. 2010. Hair by Pia Vivas. Photo: © Adam Harvey

CV Dazzle Look 4. For DIS Magazine Creative direction by Lauren Boyle and Marco Roso. 2010. Hair by Pia Vivas. Photo: ©Adam Harvey

Evaluation #

To verify the results below, the images can be tested against each of the OpenCV haarcascade profiles to demonstrate their effectiveness. Use the code below to verify the results in the table.

Results for haarcascade_frontalface_default.xml, haarcascade_frontalface_alt.xml, haarcascade_frontalface_alt2.xml, and haarcascade_frontalface_profileface.xml:

Another way to visualize the effectiveness is to use a saliency (heat) map to show which areas of the facial region are missed or activated by the face detection algorithm. The saliency map is generated by separating each stage of the haarcascade classifier and measuring the confidence score computed for that stage, then merging the sliding window regions to produce a heat map.

Saliency map for Look 5. Model: Bre. Saliency evaluation from New York Times Op-Art photoshoot. © Adam Harvey 2014.

Saliency map for CV Dazzle Look 6. Model: Jason. Saliency evaluation from New York Times Op-Art photoshoot. © Adam Harvey 2014.

The results illustrate and prove that hair and makeup alone can be used to lower the wearer's probability below the threshold of detection for most of the OpenCV Haarcascade detection profiles. There are important limitations to keep in mind though.

First, these looks were designed to work against the Viola-Jones Haarcascade face detector in 2D still-images in the visible light spectrum with the pretrained Haarcascade detection profiles. Other face detection algorithms including Lineary Binary Pattern (LBP), Histogram of Oriented Gradients (HOG), Covolutional Neural Networks (CNN), multi-camera 3D-based systems, and multi-spectral imaging systems would require a different strategy. These looks are solely based on the Viola-Jones Haarcacade classifiers.

Second, lighting conditions will cause the results to vary. The pose and illumination in these photos is similar to a biometric enrollment (passport style) photo. Overhead or more direct lighting will change the intensity and location of shadows which will change the detection outcome.

The tips below apply only to the Viola-Jones haarcascade method for face detection. For the best performance a CV Dazzle look is highly specific to the situation, unique to the wearer, designed for specific algorithm and never replicated.

1. Makeup Avoid enhancers. They amplify key facial features. This makes your face easier to detect. Instead apply makeup that contrasts with your skin tone in unusual tones and directions: light colors on dark skin, dark colors on light skin.

2. Nose Bridge Partially obscure the nose-bridge area. The region where the nose, eyes, and forehead intersect is a key facial feature. This is especially effective against OpenCV's face detection algorithm.

3. Eyes Partially obscure one or both of the ocular regions. The symmetrical position and darkness of eyes is a key facial feature.

4. Masks Avoid wearing masks as they are illegal in some cities. Instead of concealing your face, modify the contrast, tonal gradients, and spatial relationship of dark and light areas using hair, makeup, and/or unique fashion accessories.

5. Head Research fromRanran Feng and Balakrishnan Prabhakaran at University of Texas, shows that obscuring the elliptical shape of a head can also improve your ability to block face detection. Link: Facilitating fashion camouflage art. Use hair, turtlenecks, or fashion accessories to alter the expected elliptical shape.

6. Asymmetry Face detection algorithms expect symmetry between the left and right sides of the face. By developing an asymmetrical look, you can decrease your probability of being detected.

Commissions #

In December 2013 the New York Times commissioned a new look (Look #5) for an Op-Art feature about facial recognition, introducing the concept to a large audience and proving that face detection and face recognition are not as powerful as agencies claim.

Produced by John Niedermeyer and James Thomas. Photo © Adam Harvey, Modeling By Bre Lembitz, Hair By Pia Vivas, Makeup By Giana DeYoung

Background #

This project began in 2010 as my masters thesis at NYU ITP as a challenge to the growing power asymmetries in computer vision and, in particular, widespread deployment of facial recognition technology.

The name of the project was inspired by WWI ship camouflage called Dazzle that used cubist-inspired designs to break apart the visual continuity of a battleship in order to conceal its orientation and size. Similarly, CV Dazzle, short for Computer Vision Dazzle, uses bold, graphic designs that break apart the visual continuity of a face. While the end result is still visible to human observers, CV Dazzle degrades the visual comprehension of computer vision systems.

This ongoing project is motivated by a need to reclaim privacy in a world of increased visual surveillance and data collection. Computer vision poses new challenges that otherwise did not exist in human observation; it is scalable, remote, networked, magnified, and multi-spectral.

Ideally, there would be a way to appear visible to human observers but less visible to computer vision surveillance systems. This is the goal of CV Dazzle; to mitigate the risks of remote and computational visual information capture and analysis under the guise of fashion. Since beginning this project in 2010, the concerns of a widespread facial recognition have only become more urgent and apparent and hopefully this project will continue to develop.

There has been a surprisingly positive response to the CV Dazzle project since first publishing it 2010. Activist and art groups have done workshops to explore and expose the vulnerabilities of biometric recognition systems. Both adults and children have done makeup parties. Fashion designers, musicians, political activists, and hackers have modified or borrowed concepts and taken the original ideas into weird new places. It's encouraging to see how concepts spread over distance and time, and to see a steadily growing resistance to face recognition.

There have been some confusing representations of the project and many questions. Although the project is from 2010, I'll continue to update this page with responses and new looks when available. Unfortunately, there is a lot of copy-paste journalism these days without sufficient fact checking.

FAQ and Fact Check #

When was CV Dazzle created?

There is a Wikipedia page about this project that contains some inaccurate second-hand journalism and derivative crowd-sourced information. CV Dazzle was created for my thesis project at New York University's Interactive Telecommunications Program in 2010 under the advisement of Despina Papadopolous.

Does CV Dazzle still work?

Several articles make the claim that CV Dazzle "doesn't work" for them. Most likely it doesn't work because an outdated pattern was used on a newer algorithm.

Looks 1 - 5 were all designed for the Viola-Jones Haarcascade algorithm, which has been unofficially deprecated since around 2016, and probably deprecated between 2013-2016 in security systems. All face recognition systems, which rely on face detection as the first step, now use convolution neural networks. To be clear, this site has advised against using looks 1-5 since 2016. As with all camouflage tactics there is a window of opportunity and that window has passed for looks 1 - 5. Please reference thew new looks 6 and 7 for advice on breaking CNN-based face recognition, which requires breaking the key geometric relationships of facial components.

The key point is that CV Dazzle is not a specific pattern. Consider disruptive pattern material (DPM)camouflage, and how there are many different variations for different terrain. DPM is a technique, not a specific pattern. CV Dazzle is a also a technique, not a specific pattern.

Will there be an updated version?

The main goal of this project was to develop a proof-of-concept that showed how faces could exist in a dual perceptual state: visible to humans but invisible to machines. This is now well established and there are many other approaches, including adversarial attacks, that achieve the same result on neural networks.

The value of camouflage against computer vision and also the brittleness of many algorithms is now well established, even within intelligence agencies. For example, here's a video of the former director of the United States Intelligence Advanced Research Projects Activity (IARPA) in 2017 (7 years after CV Dazzle was published) describing his interest in "fairly easy ways to spoof facial recognition" by "you know, taking a magic maker and putting a couple dots on your forehead." The tactic of using makeup-based camouflage to break facial recognition systems, of which face detection is the first step, has been well studied by intelligence communities around the world, including references to this project.

Face detection and face recognition are not the same thing

The second misconception is that the original CV Dazzle designs were supposed to break face recognition. As noted above, the original CV Dazzle designs targeted "face detection" algorithms, not "face recognition".

Face detection and face recognition are completely different algorithms, though they are often conflated by non-technical writers. It is possible to design a look against face detection or face recognition, but they are different algorithms and require a different design. It would also be possible to design a pattern against emotional recognition, age estimation, cardiopulmonary analysis, iris recognition, gait recognition, gender estimation, body pose estimation, and any other biometric computer vision algorithm. But that algorithm should be clearly specified to a reader to prevent further misunderstandings. Any article failing to mention a specific algorithm should be dismissed.

Academics critical of counter-surveillance

Another occasional criticism is that CV Dazzle, and by extension any personal act of resistance, is too individualistic. This perspective should be avoided because it discourages experimenting and probing opaque algorithmic systems. It's important to question these systems, attempt to break them, and explore them on one's own terms. More effort is needed to counter mass surveillance from both individuals and groups. Supporting one does not negate other. Collective action can emerge from shared personal affinities and encouragement instead of academic cynicism.

A strong response to mass surveillance should include both legal and technical counter-surveillance tools.

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CV Dazzle is a computer vision camouflage strategy developed in 2010. Unlike disruptive-pattern material, CV Dazzle is bright and bold, and evades detection by breaking weaknesses in automated pattern recognition systems.

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