THE C-LOUD Ali Fatih Cebeci Dilara C-LOUDTHETurgut
THE C-LOUD
THE FacultyTUC-LOUDDelftof Architecture and Built Environment Complex Projects Datapolis 2021 Ali Fatih Cebeci Dilara SpecialTurgutthanks to the face model of the project
THE C-LOUD 0 Research R1.- 00_Origin R2.- 0 to3-...And2-1-Mechanics1DailyEncounterstheothers R3.- Waking Up 1- Timeline of datasets 2- Counter Surveillance R4.- Justice League
THE C-LOUD 1 Design D1.- Hard Decisions 1- Barriers of Face Recognition 2- United Nations 3- Californian ideology-Ayn Rand D2.- Proposal1-Mechanics2-C_loud3-HolographWall
THE C-LOUD Microsoft president Brad Smith says the dystopian reality George Orwell depicted in his book 1984 “could become a reality in 2024”1. Today we are living in a physical world but we all already exist in the online world. We have every data that makes us human, maybe even everything that makes us who we are online. We are not in the cartesian world anymore, you “might say that the Cartesian subject has become a virtual one”2. The vast amount of data online is used for the sake of security of the public through surveillance. Situations that threaten public safety can be tracked and prevented through surveillance. We mostly naturally “want perfect safety and perfect privacy but those two things cannot co-exist”3. With facial recognition technology combined with our online data, what once was just skin and bones, our face now becomes a façade to our identities, our thoughts, our past and our future, to our virtual subjects. It becomes a public object, and our privacy is therefore invaded. The given anonymity on a physical level is slowly disappearing for the sake of security provided by surveillance. In this new era “opacity is the new oil”4. The will to be anonymous on the street but actively using the perks of the online world pushes individuals to take counter surveillance actions on a personal level mostly by manipulating the architecture of the face. C-loud visions ever persisting battle of surveillance and countersurveillance assigning role as good and bad when this kind of blinding power of knowledge is regulated. We propose a new body to the United Nations for the regulation of surveillance. This intergovernmental, AI based mediating organization controls, regulates and if necessary, eliminates surveillance agencies. Main headquarters of the organization is in New York within the UN center and just like the UN peacekeeping missions it has several bases of operations equipped with ready and communication between the 6
THE C-LOUD 7 organization and society is carried through a mechanism. AI decision mechanism is based on Ayn Rand and Californian ideology which argues that machines can create a stable world without hierarchy5. They will be deployed when the society of of surveillance and when observed, excessive surveillance is detected. For elimination or decrease of surveillance we be generated by the c-louds. A new public the resolution but the recognizability of a face. A unique environment in the physical world created by holograms to help people stay anonymous. When in action C-loud observes the environment for surveillance and movement of the people .C-louds are eclipses of surveillance; they cast a shadow on the street with changing hologram architecture on the street. This hologram wall creates a virtual layer of counter surveillance on the faces of the people, disabling facial recognition systems to identify landmarks of the face hence recognizing who that person is. The operations of C-louds tackle the border in between the eyes of any machine and the face of the individual. The project promises a heterotopia where the old luxury of anonymity and the new fashion of knowledge can exist together.
THE C-LOUD 0
THE C-LOUD RESEARCH
THE C-LOUD “DATA IS THE NEW OIL” Clive Humby, 2006 “OPACITY IS THE NEW OIL” Jamais Cascio, 2012 Data is(sic) the New Oil, San Fransico 10
Jamais Cascio Panopticon”
“Participatory
“What called
happens when you combine mobile communications, always-on cameras, and commonplace wireless networks? I
THE C-LOUD
11
the answer the Participatory Panopticon.”
THE C-LOUD12
Edward Snowden “Privacy Is For The Powerless”
THE C-LOUD
WHISTLEBLOW ON NSA SURVEILLANCE
“Do we want to live in a society where we live totally naked in front of government, and they are totally opaque to us?”
Former NSA computer intelligence consultant leaked information about NSA surveillance on Imageciviliansfrom The Guardian 13
THE C-LOUD14
THE C-LOUD RESEARCH STATEMENT The spaces of human rights violation (from privacy point of view) with respect countersurveillance in the scope of face recognition Actors: Surveillance - Counter Surveillance : The war between surveillance and counter surveillance Scope: Face recognition : Human rights 15
THE C-LOUD 00_Origin 16RESEARCH
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THE JeremyC-LOUD
Bentham’s radical design for a new prison, panopticon, has been highly criticised and compared to traditional institutions and life of a person by the French philosopher Michel Foucault. It is not simply a prison design but, Panopticon is a conceptual design with a central watch tower for surveillance demonstrating an asymmetrical power relation between the watcher and the object. Today, in the era of data we are constructing our own panopticon with the data being the central point of power. We share data online to use the services a modern person need to survive in daily basis, but we do not actually know where our data ends up. We “even at home, (…) have to scan our phone”6. “When the complexity of life is reduced to numbers, data, bits, calories, pixels, characters, and likes, the whole of man becomes information”7. When this data is collected and stored, we can form online personalities of everyone. ”Ironically, it is called BIG data, considering its enormous volume, velocity, and variety”8. We do not really feel the existence of the ‘big’ Data or maybe ‘Big’ brother of the Orwellian world. We do not experience it, as dystopic as Orwell delineates; the agents have transformed themselves into the presence of small gadgets that we welcome -in fact we cannot live without- in our everyday lives. This researh focuses on the spaces of and counter surveillance. It tackles the point of view in order to understand the complex network between surveillance and counter surveillance through data and face recognition.
THETORONTO,C-LOUD CANADA 9,59 CCTV / KM 2.78 CCTV / 1000 PEOPLE RIO DE JANEIRO, BRAZIL 11.47 CCTV / KM 1.04 CCTV / 1000 PEOPLE BAGHDAD, IRAQ 178.31 CCTV / KM 16.8 CCTV /1000 18.15HONGPEOPLEKONGCCTV/KM 6.62 CCTV /1000 39.5TOKYO,PEOPLEJAPANCCTV/KM1.06 CCTV /1000 658.27CHENNAI,PEOPLEINDIACCTV/KM25.52 CCTV / 1000 PEOPLE BUENOS AIRES,77.34ARGENTINACCTV/KM 1.04 CCTV /1000 PEOPLE ST. PETERSBURG, RUSSIA 38.27 CCTV /KM 10.07 CCTV / 1000 399.27LONDON,PEOPLEUKCCTV/KM67.47 CCTV / 1000 PEOPLE SEOUL, KOREA 67.55 CCTV / KM 4.1 CCTV /1000 PEOPLE NEW YORK, USA 25.97 CCTV / KM 3.78 CCTV /1000 277.51BEIJING,PEOPLECHINACCTV/KM56.2 CCTV / 1000 PEOPLE 18
THE C-LOUD CCTV DENSITY MAPS Locations and density of CCTV cameras in different cities. Graphics are based on the number of CCTV cameras per 1000 people. Illustrations by the authors Data from the internet SYDNEY, AUSTRALIA 4.85 CCTV / KM 12.18 CCTV / 1000 PEOPLE ROME, ITALY 6.35 CCTV / KM 1.92 CCTV / 1000 PEOPLE MEXICO CITY, MEXICO 58.59 CCTV / KM 3.99 CCTV / 1000 PEOPLE TEL AVIV, ISRAEL 24.47 CCTV / KM 1.03 CCTV / 1000 PEOPLE PARIS, FRANCE 254.59 CCTV / KM 2.44 CCTV / 1000 PEOPLE ISTANBUL, TURKEY 42.3 CCTV /KM 7.18 CCTV /1000 PEOPLE BERLIN, GERMANY 19.6 CCTV /KM 4.9 CCTV / 1000 SHANGAI,PEOPLECHINA250CCTV/KM36.96 CCTV / 1000 PEOPLE 19
THE C-LOUD LONDON, UK 399.27 CCTV / KM 67.47 CCTV / 1000 PEOPLE 20 CASE STUDY I LONDON, UK
THE C-LOUD LONDON Images from the internet 21
THE C-LOUD LONDON Big Ben surveillance cameras Image from the internet There are 691,000 CCTV cameras in London (2020/2021)Ratcliffe There is 1 CCTV camera for every 13 people. Population: 8.9 Million Ratcliffe Average Londoner is caught on camera 300 times a day. Ratcliffe 23
THE C-LOUD NEW YORK, USA 25.97 CCTV / KM 3.78 CCTV /1000 PEOPLE 24 CASE STUDY II NEW YORK, USA
Photo by Alexandra Schuler
25
THE C-LOUD NEW YORK Images from the internet
THE C-LOUD HONG KONG 18.15 CCTV / KM 6.62 CCTV /1000 PEOPLE 26 CASE STUDY III HONG KONG
THE C-LOUD HONGKONG Images from the internet 27
The Guardian 29
Photo by Cocoa Laney/The
Observer
THE C-LOUD COUNTER SURVEILLANCE MAKE UP Protesters wearing makeup to mislead facial recognition cameras
“Privacy campaigners bid to beat police facial recognition plans by wearing ‘dazzle’makeup.
Illustration
The
Yorker 31
STEALTH WEAR
“‘’As cities become ever more packed with cameras that always see, public anonymity could disappear. Can stealth streetwear evade electronic eyes?’’ John Seabrook” New
THE C-LOUD
“Is there anything fashion can do to counter the erosion of public anonymity?”(Ana Galvañ) by Ana Galvañ
THE C-LOUD HYPERFACE Hyperface pattern contaning thousands of false face recognition hits
The Guardian 33
Photo by Adam Harvey
“Anti-surveillance clothing aims to hide wearers from facial recognition.”
THE C-LOUD 0 TO 1 34RESEARCH
THE C-LOUD FACE SEEN FROM A CAMERA Image by the authors
Surveillance is not new for humanity, but the meaning of the term is slowly shifting. While surveillance means observation for data collection today we are living in a world of surveillance created by data given by ourselves rather than collected from us. With the development of internet today we share our data unknowingly to be able to use the services of such platform. Until now this information we share online has not been a big issue of surveillance since we were able to hold on to our anonymity in the real Developmentworld.of the facial recognition systems cracked the layer of anonymity of the real world and exposed our faces to the online world. We are not able to hide from the data we share online in our daily life. Machines and algorithms can identify and track people on the street and match their data. Blind surveillance alghoritms now have eyes that sees everything.
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THE C-LOUD _activatephotographreal-lifedrawing 36
THE C-LOUD primary visual cortex amygdala fusiform face area ahippocampus occipital face area is_detectionitaface? facelandmarks_geometryofthe emotionsexpressions_facial sulcussuperiorgazeexpressions_facialdirection,mimicstemporal
HIDDEN POWER OF HUMAN BRAIN
Thales Group Human beings are already unconsciously familiar with the mechanism of the facial recognition system since they inherently the human brain called the fusiform face face recognition, which was proved recognition system was developed. It has been observed that this area, located in the inferior temporal lobe, becomes activated when we see faces. That is to say, it can decide if it is a face or not. Yet, it is still questionable whether this area responds to only human faces or rather complex systems. Since we are surrounded by humans, our database is mainly composed of their faces. The fusiform face area does not run the facial recognition process alone.
Illustration by authors “In 2014, Facebook announced its DeepFace program, which can determine whether two photographed faces belong to the same person, with an accuracy rate of 97.25%. When taking the same test, humans answer correctly in 97.53% of cases, or just 0.28% better than the Facebook program.”
The occipital face area(OFA), which is believed to receive the data before FFA, determines the geometry of the face; in other words, the landmarks of the face. The superior temporal sulcus(STS) is able to comprehend the gaze movements and the mimics, and the amygdala processes the information of emotions. Eventually, in the hippocampus, the face is linked to the memory and saved in our database. Even though facial recognition algorithms work with similar to the anatomy, how the human brain recognizes other faces is founded in late 90’s while computerized basic facial recognition systems are being used for37
THE C-LOUD Histogram of Oriented Gradients 38
First the image is converted into a black and white drawing and a force number is assigned to each pixel according to the brightness value of the pixel. When the whole image is converted into brightness values, computer can assign a vector and direction to each pixel depending on the surrounding pixels. These vectors create an image called Histogram of Oriented Gradients (HOG). With this data, algorithm can detect an edge when there is a sudden change in HOG. When a HOG of a regular face is previously introduced to the algorithm it can now detect faces in an image.
THE C-LOUD FACE DETECTION Illustrations by authors For a face recognition algorithm to work image of the environment into digital data. Just like the perspective representation crafted in the Renaissance to better communicate the drawing to the spectator, computer needs to convert this digital data into a new image that the algorithm can understand. This is process and it is called face detection.
Histogram of Oriented Gradients 39
THE C-LOUD 68 Facial Landmarks68 Facial Landmarks 40
THE C-LOUD NORMALISATION Illustrations by authors Second step of the face recognition process is called normalization. This process is a preparation process for the last step. Face recognition systems are mostly trained by photos taken from front but that is not the case of faces that is caught by cameras in the real life. For face recognition algorithms to work has been detected. Computer assigns points to landmarks of a face. An average of 68 point is assigned to facial landmarks such as the eye lines, ear line, eyebrows, nose and chin. Number of points assigned to a face depends on the algorithm. With these points the computer can shift, tilt and rotate the image to normalize the points. Rotation of the image 41
Convolutional Neural Network pathsRGB values of the colored image
THE C-LOUD
42
Convolutional Neural Network scans 43
THE C-LOUD FACE RECOGNITION Illustration by authors
Last step of the process is face recognition and categorization of the face. The normalized face is turn back to colored. When in colored view each pixel contains an RGB value according to the color of the pixel. Each red, green, blue value creates a layer for program to scan the image. Computer uses an algorithm called convolutional neural network to scan the image for unique features of the face. CNN can create countless paths to scan the image. This system creates (size depending on the system) a matrix of values which is a path and scans the image with path. Computer can create very complex matrixes to better analyse the image by scanning each layer countless times. When the scan is complete algorithm extracts the unique features of the face and locates the face in a 128-dimensional coordinate system with a score assigned to the scanned unique features of the face. Next time when a similar score is assigned to a face in the coordinate system algorithm matches the database of the face to the face in the image.
Illustration by the authors
THE C-LOUD DAILY ENCOUNTERS
Today face recognition is slowly becoming one of the main ingredients of the modern day. We use our face to unlock our phone in the morning, interacting with other people. Some shops are using face recognition to track customers. Some banks have started to use facial recognition technology to increase their security. Even some hospitals match medical records with the face of a human to track medical history of the patients. While we everyday these systems are actually the end point of a complex network of data. We are being tracked by data collecting machines constantly during the day. With the data they collect and the data we agree to share with applications, we give away a vast amount of personal data. Facial recognition systems are able to match these previously provided data with the face in real time. 44
THE C-LOUD airport cameras 45
THE C-LOUD _name, _activity_address*_location_phone_email_date_usernamesurnameofbirthadressnumberdateand time _acquaintances_device_interests public publicprivate _databaseindividual _sleeping _health_metabolismtime_scheduleinformation _Apple ID _email address _app _purchase_iMessage_photosdata history _email address _app _purchase_photosdata history _location_payment_photos_contacts_activity_health_photosinformationinformation _name, surname _date of _work_contacts_CV_location*_educationbirthplaces _name, surname _email address _date of _interests_location*birth _name, _acquaintances_interests_activitydate_account_tweet_location_phone_email_date_usernamesurname**ofbirth*addressnumberlocationcreationdate,time _name, _size_number_login_content_read,_status_size_IP_time_date_phone_email_date_usernamesurname**ofbirth*addressnumberaddressofemailofemailrecipient,requesttypeandencodingrecordsofattachmentsofattachments_search queries _results from queries _date and time of activity _visited pages _IP _timeaddressofactivity 30 days _interests_payment_email_usernameaddressinformation* _databasegovernement | law enforcement _database_databaseatabase_database e_a_Aepi_ea 46
THE C-LOUD airport cameras _duration of call _number of call _time of _numbercallofevery caller _serial number of devices _audio_location _camera settings _camera make and model _resolution_photo_location*_photographsize 30 days_name, surname _social ID _date of _date_movement_location_plate_addressbirthnumberandtime of the movement _name, surname _social ID _date of birth _bank account number _interests_transactions _name, surname _email _interests_credit_shopping_shippingaddressaddressbehaviourcardinformation _name, surname _social ID _date of _health_addressbirthrecord _email _interests_appointments_path_movement_location_address*address* _name, surname _social security number _passport number _date of _criminal_finger_addressbirthprintrecord _b_d_s_ntr _cameracameramaksett addres dd * ...AND THE OTHERS Interlinked network of online personal data and everyday face recognition surveillance encounter Illustration by the authors 47
THE C-LOUD Waking Up 48RESEARCH
THE C-LOUD CODED BIAS AI based face recognition alghorithms Image from Coded Bias,Shalini Kantayya Face recognition systems have changed the secrecy of the face. Our faces are now fronts to our online data, online selves. Once sacred, our faces are now borders between the physical and the online world and it is not always being used with our Faceconsent.recognition algorithms needs to be trained with many training photos. It is important the faces for the training data sets are different in many aspects so that the algorithm can be trained properly for all aspects. They also need to be with different conditions of lighting or face position so the computer can work better in the real world. Most of the face recognition training data sets are online research. The lack of an aspect in the images of the data sets (i.e. less women, black people or asian people) can create false recognition hits which creates bias in the results of the algorithm. Many companies and scientists are looking for new ways to create bigger and better datasets. In 2015 Facebook found a new technique to use user photos for their own facial recognition software hence created one of the biggest and ever-growing datasets. The social media app updated their terms and conditions mentioning the face recognition in a short part. Facebook claims this information is not shared with third parties, but this doesn’t change the fact that Facebook can match a face on the street with all the data the application has ever collected about that person. Some people do not want to be recognized on the street and hence they take counter surveillance measures. These measures are mostly on a personal level by manipulating the face so that the recognition systems cannot detect the face. Face recognition is an advanced technology but it is fragile. Stripes of paint on strategic location of the face can fool the algorithm. 49
THE C-LOUD _1970_1965_1920 _1940_1880 _1900_1860_1820_1840 _1960 183818438 _ERA(_%100-PhotoI shot) Digital Face Recognition _Alphonse Bertillon's Synoptic _First Hints for face recognition Table of Physiognomic Traits Manully measured file of Criminal faces for police use _First photos taken to be used for reIdentification of prisoners _First1839BELGIUMPhotocontainingahumanbeing_First1826-27Photo ever taken _First1846photos taken to be used for reIdentification of prisoners LONDONNEWYORK1857_Firstphotos taken to be used for reIdentification of prisoners 1870 1909 _Pinkerton National Detective Agency use of wanted posters created the largest collection of mug-shots _First1942CCTV camera Siemens AG Used for observing rocket launch _First1949commercial CCTV camera system Designed by an American Company _NY197Tim _Manual1960 measurement by Woodrow Bledsoe First face recognition attempt with facial landmarks location landmarks on a face _First1964computerized face recognition vtechnology with rand tablets Computerized alghoritm is applied afte _Advencing1970 th Introducing 21 to increase the _Invention1970 of V Recording the Mugshot photography is the basis of facerecognitionthe system datasets. It is used to identify identifiedmugshotscriminal.Beforecriminalswerebymemory. Alphonse Bertillon’s catalogue of landmark dimensions of different people First hints for face recognition. Bertillon created a manually measured file of criminals faces for police use. Criminals could be identified manually by comparing a photo and the Face containinalghorithdatasetssystemsrecoutmg and technologdevelopedFacesandbiasedcertaintrainfrontalthealpreresuvDatasinthey Manual Face Recogniton 088 090068 aphy is efdstemtooreswereory. First ever taken Mugshot perciuse.carecognitiomanualriminalsCrimdentifiedomparinson 50
THE C-LOUD _1975 _1980 _1985 _1990 _1995 _2000 _2005 _2010 _2015 _2020 _ERA(_%9,3III -Other) (_%6,3 -CCTV Cameras (_%15,6-Mug shot) (_%37,5-Web (_%31,3-PhotoSearch)Shot) _ERA(_%4,3IV -Other) (_%4,3 -CCTV Cameras) (_%4,4 -Mug (_%8,7(_%78,3-Webshot)Search)-PhotoShot)((_%11,1-Other)(_%2,8-WebSearch)_%86,1-PhotoShot) _First compuatational dataset Pictures of Facial _60images&_10peopleaffect Lorem ipsum _Labeled Faces in the _14.1kImages&_1.2kPeople_121.6kImages&_37.4kPeople_FRVT_13.2kImages&_5.7kPeopleWild2002FERET _Facebook does not share their data _Facebooks breakthrough with deeplearning _Celeb _50mImages&_500kPeople500k 73PD installed set up cameras in es square to monitor r a person e accuracy of Face Recognition subjective facial markers in order automation of the system v VHS Tapes e CCTV Footage _Using1980 of linear algebra for facial recognition _First2000camera in cell-phone _Facebooks2000 breakthrough with deeplearning in facephotosrecognitionforthedatasets_Scrapingfacebook2007_WebsearchforFACEdatacollection2006_Facerecognitiongrandchalenge_First1996large scale dataset for academic and commercial research CCTV cameras were designed to monitor rocket launches sesgnitionotrain the m. Datasets g only studio light photos failed to ghorithms up to a cision and cause ults. CCTV footage sets like Labelled e Wild further ythe Facebook started using the user uploaded photos of their online platform hence creating the one of the largest dataset for their own face recognition systems. Number of people in the dataset Number of photos in the dataset No data available 000 005 001 otos of m hence ftheirthe ion NNNLorem ipsum 51 TIMELINE OF DATASETS Historical development and rise in digital and physical face recognition data sets Illustration by authors
WILLIAMS
Photo
THE C-LOUD
Kashmir
First known case of a person accussed of a crime he did not commit due to malfunctioning facial recognition system hit from the Internet
“Wrongfully Accused by an Algorithm” Hill New York Times
Robert
York Times 53
“This is not me. You think all Black men look alike?” Julian-Borchak Williams New
ROBERT JULIAN-BORCHAK
THE C-LOUD54
THE C-LOUD YOUR FACE IS BIG DATA
Face recognition algorithms and face recognition data sets are online for research purposes. Everyone can download the algorithm and train it with the datasets and use it with any photo. A Russian researcher used a face recognition algorithm he found online and trained it and used it with photos he has taken randomly on the subway. people’s social media accounts.
Photos from Egor Tsvetkov’s exhibition
“My project is a clear illustration of the future that awaits us if we continue to disclose as much about ourselves on the internet as we do now.” Egor Tsvetkov 55
THE C-LOUD
“For facial recognition algorithms to work well, they must be trained and tested on large data sets of images, ideally captured many times under different lighting conditions and at different angles.”
Richard Van Noorden “The Ethical Questions That Haunt Facial-Recognition Research”. “Machine learning-based systems are trained on data. Lots of it.” Heilweil “Why Algorithms Can Be Racist And Sexist”
THE C-LOUD DATA WITHOUT CONSENT used in MegaFace data set Image by Adam Harvey
Rebecca
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THE C-LOUD58 private law databaseenfrocementsocialmedia databasespublicclearview
THE C-LOUD “Clearview AI has created the world’s with over 3 billion images sourced from the public internet, including news media, mugshot websites, public social media, and many other open websites.” Clearview AI CONSENTFUL IMAGE COLLECTION? Illustration by the authors 59
THE C-LOUD60
Consent 61
Dilemma of
THE C-LOUD FACEBOOK HELP CENTER Facebook Terms&Conditions on face recognition “It would take 76 work days (8 hours a day) for the average person to read the Terms and Conditions they agree to in a year.” Rich Powell “Ever Wondered How Long It’d Take To Read All T&Cs You Agree To?”
Image
MEREDITH House hearing on societal from the Internet
Ocasio-Cortez: Can surveillance camera footage that you don’t even know is being taken of you be used for facial recognition?
Ocasio-Cortez:
Ocasio-Cortez: Could using a Snapchat algorithm for facial recognition?
ethical and
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THE C-LOUD
Whittaker: Absolutely – by Facebook and potentially others.
Whittaker: Absolutely.
Whittaker: Yes, and cameras are being designed for that purpose now.
WHITTAKER
So if you’ve ever posted a photo of yourself to Facebook, then that could be used in a facial recognition database?
THE C-LOUD64 Consent Required Do you accept that your face will various databases?
THE C-LOUD 65 l be used in facial recognition system in Yes NextNo >> x?
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001010101001010101010101010101001010101001010101010101010101001010101001010101010101010101001010101001001010101010101010101001010101001010101010101010101001010101001010101010101010101001010101001010101101101010101010100101010100101010101010101010100101010100101010110100101010100101010101010101101001010101010100101010100101010101010101010100101010100101010110100101010100101010101010101010100101010100101010010100101010101010101010100101010100101010110100101010100101010101010101010100101010100101010101010101010101010010101011010010101010010101010101010101010010101010010101010101010101010010101010010101010101010101011010010101010010101010101010101010010101010010101010101010101010010101010010101010101010101010101001010101001010101010101010101001010101001010101010101010101001010101001010101101001010101001010101101001010101010101010101001010101001010101010101010101001010101001010101101001010101001010101010101010011010010101010010100101001010101010101010101001010101001010101101001010101001010101010101010101001010001010101001010101010101010101001010101001010101010101010101001010101001010101010101010101001010101001001010101010101010101001010101001010101010101010101001010101001010101010101010101001010101001010101101101010101010100101010100101010101010101010100101010100101010110100101010100101010101010101101001010101010100101010100101010101010101010100101010100101010110100101010100101010101010101010100101010100101010010100101010101010101010100101010100101010110100101010100101010101010101010100101010100101010101010101010101010010101011010010101010010101010101010101010010101010010101010101010101010010101010010101010101010101011010010101010010101010101010101010010101010010101010101010101010010101010010101010101010101010101001010101001010101010101010101001010101001010101010101010101001010101001010101101001010101001010101101001010101010101010101001010101001010101010101010101001010101001010101101001010101001010101010101010011010010101010010100101001010101010101010101001010101001010101101001010101001010101010101010101001010001010101001010101010101010101001010101001010101010101010101001010101001010101010101010101001010101001001010101010101010101001010101001010101010101010101001010101001010101010101010101001010101001010101101101010101010100101010100101010101010101010100101010100101010110100101010100101010101010101101001010101010100101010100101010101010101010100101010100101010110100101010100101010101010101010100101010100101010010100101010101010101010100101010100101010110100101010100101010101010101010100101010100101010101010101010101010010101011010010101010010101010101010101010010101010010101010101010101010010101010010101010101010101011010010101010010101010101010101010010101010010101010101010101010010101010010101010101010101010101001010101001010101010101010101001010101001010101010101010101001010101001010101101001010101001010101101001010101010101010101001010101001010101010101010101001010101001010101101001010101001010101010101010011010010101010010100101001010101010101010101001010101001010101101001010101001010101010101010101001010001010101001010101010101010101001010101001010101010101010101001010101001010101010101010101001010101001001010101010101010101001010101001010101010101010101001010101001010101010101010101001010101001010101101101010101010100101010100101010101010101010100101010100101010110100101010100101010101010101101001010101010100101010100101010101010101010100101010100101010110100101010100101010101010101010100101010100101010001010101010101010101001010101001010101010101010101001010101001010101010101010101001010101001010101101101010101010100101010100101010101010101010100101010100101010110100101010100101010101010101101001010101010100101010100101010101010101010100101010100101010110100101010100101010101010101010100101010100101010010100101010101010101010100101010100101010110100101010100101010101010101010100101010100101010101010101 _Stealt _Defense_FabricWeardesign _CV Dazzle_Make-up design _Defense _Face_WholeMaskFace Mas _Defense _Face Recognition Jewelry _Attack_Face_DefenseRecognition COUNTER-SURVEILLANCE Measures taken in order to eliminate or manipulate face recognition surveillance cameras Illustration by the authors
THE C-LOUD Justice League 68RESEARCH
The research interprets the complex relation in between surveillance and counter surveillance as an ever-continuing battle. This battle is depicted in this section with face recognition in the middle as the tool with which each side can act or counter act whenever necessary. In the Justice League, the facial recognition system is represented as an objective mechanism. The data acquired from the faces can be turned into information by each side; that is to say from 0 to 1, or the truth of the face can be distorted which leaves question marks behind. The
THE C-LOUD ANALYSED-UNANALYSED Illustration by the authors 69
Revis “The Many Faces Of Today’s Facial Recognition Technology” SURVEILLENCEAirport facial recognition security system in Dulles International airport in PhotoVirginiafrom the Internet SCORE=1 70
THE C-LOUD
“At airports the use of facial recognition has proved to both enhance security as well as speed up processes such as check-in”
Richard
THE C-LOUD SCORE=1 COUNTER SURVEILLENCE
“Capture” An artists collection of facial sourcing through an online platform Photo by Paolo Cirio
The Guardian 71
“We’re now approaching the technological threshold where the little guys can do it to the big guys,”
THE C-LOUD
Carter 72 SCORE=2
“In New York, police were able to apprehend an accused rapist using facial recognition technology within 24 hours of an incident where he threatened a woman with rape at knifepoint.”
Photo by Eric
Bernard Marr “Facial Recognition Technology: Here Are The Important Pros And Cons” SURVEILLENCE Proper use of facial recognition technology on criminal activity
THE C-LOUD
(@alessabocchi) COUNTER
73 SCORE=2
“Hong Kong protestors are on another level. Here they’re using lasers to avoid facial recognition cameras. A cyber war Houser HouserHong Kong Protesters Use Lasers To Block Facial Recognition Tech SURVEILLENCE trying to blind cameras by pointing towards
cctv
laser lights
Kristin
Photo by Marcelo Hernandez
Demonstrators
camera lenses
THE C-LOUD PRIVACY_scoreSurvelliance0 front camera cctv camera _government/lawenforcement traffic cameras airp selfie social media g asclor dr face id privatepublicpublicprivateappleindividualclearviewgoogle google earth gnikcahgnikcah linkedin face filters urban-ciretailretailcamerascamerastycameras li linkedintwitter googleearth whatsappyoutubefacebookinstagramfacebook snapchat 0 0 ?0? ? 0 0 0 0 000011111111111 1 111 1 1111 PRIVACY 74 2
THE C-LOUD Countersurveillance_score_attack_defence0 back beingphotographing/camerafaceidphotographedselfie_store_save educational institutions camerasbankcamerashospitalcameras face face facejewelrymaskmaskstripe make-up face hyperpaintfacefacelightumbrellaalleryort loud and rocialchive ive no wrongtaggingtaggingscreenshot umbrella on cameras spray paintlaserprivatepublic gnikcahd al spraypaint 0 00 ? ?? ? ?? ?? ??? ? 0 0 1 1 0 JUSTICE LEAGUE 75 2
THE C-LOUD 1
THE C-LOUD DESIGN
“I’m constantly reminded of George Orwell’s lessons in his book 1984. You know the fundamental story…was about a government who could see everything that everyone did and hear everything that everyone said all the time.”
“Well, that didn’t come to pass in 1984, but if we’re not careful that could come to pass in 2024.”
79 V
THE C-LOUD GEORGE ORWELL 1984 George Orwell’s book of a dystopian science Photo of the book cover 1984
Microsoft president Brad Smith
SAFETY GOVERNANCE 80 JOHN OLIVER
John Oliver “Last Week Tonight with John Oliver”
“We all naturally want perfect privacy and perfect safety but these two thing cannot co-exist”
THE C-LOUD
“The tools of surveillance are almost completely unregulated” “Inside the massive (and unregulated) world of surveillance tech”
THE C-LOUD
PRIVACY OPERATION 81 SHARON WIENBERGER
Sharon Weinberger
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THE C-LOUD
DESIGN STATEMENT Tackling the cameras as borders in between physical and digital world to better understand the visibility of face and the regulations concerning the technology of facial Illustrationrecognitionbythe authors 83
THE C-LOUD Hard Decisions 84DESIGN
The design visions a mediatory role on the regulation of facial recognition technology. It considers the space two worlds of surveillance take place and tackles the issue of camera from the visibility point of view. Cameras are the border lines in between the algorithms and individuals. Even though their existence is a demonstration of control, their visibility determines surveillance.
85
THE C-LOUD CONCEPTUAL COLLAGE Illustration by the authors
Surveillance is a fragile topic and should be interpreted carefully. We are living in a surveillance world already but if we are not careful enough, we might end up living in an dystopian world as there is a brittle border in between safety and privacy when it comes to surveillance. Surveillance is a powerful tool and if not controlled, it might cause more harm than good in wrong hands. When referring to face recognition technologies, one cannot deny the power of such surveillance tool but when investigated deeply it is seen that this kind of power is almost unregulated in every country. Even if there is a regulation for this technology it is only regarding to the ban of use rather than governance. This complete obstruction on surveillance recognition technology. If regulated properly and controlled accurately facial recognition technologies can be very the same; who can we trust in governing with such blinding power?
THE C-LOUD DISINTEGRATE A FACE “New York Times, How the Police Use Facial Recognition, and Where It Falls Short” (Yoshi Sodeoka) Image by Yoshi Sodeoka “Resolution determines visibility.... Whatever it is not captured by resolution is invisible” Hito Steyerl 87
THE C-LOUD _Digital World Image analysis Barrier_Digital Barrier_Digital 88
THE C-LOUD _Physical World Image Capturing
Illustration by the authors Face recognition technologies are the connection between the digital subject with individual’s face. This process happens through different layers of mechanisms, travelling from a face to a computer with cameras in the middle. These layers of mechanisms create different barriers for face recognition systems. When it comes to counter surveillance, the digital barriers are hard for an individual to block and hence most counter surveillance techniques manipulate the structure of the face tackling the personal barrier. C-loud tackles the environmental barrier to shape the surrounding of the cameras visible range, to block the view. Environmental barrier is harder for an individual to exploit but for sure is more affective in counter surveillance for bigger groups of people. 89
BARRIERS OF FACE RECOGNITION
Barier_Environemental Barrier_Personal
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THE C-LOUD
91
UNITED NATIONS International court of justice
Photo by UN United Nations is an intergovernmental organization amongst countries for international peace keeping and countries. It is founded in 1945 after the second world war and has been actively working as an independent mediator in between countries. Headquarters of United Nations is located in New York but the organisation oparates mediatory peace keeping missions through various local stations.The C-loud is visioned to be part of the United Nations due to its independent mediator role but since the human is a naturally biased source of decisions, the organization of the C-loud part of United Nations. The organization will be located at the main headquarters of the UN but the management is left for reactions.
THE C-LOUD _MIPONUH1997_UN HEADQUARTERS _MINURSO1991- _MINURCA1998_UNOMSIL1998- _U19 92
THE C-LOUD _UNMOB1996- _UNIFIL1978-_UNKOM1974- _UNIKOM1991-_UNMOT1994- _UNMOGIP1949_UNTSO19489NMIBH95- _UNFICYP1964UNITED NATIONS Currently active UN peace-keeping mission headquater locations Illustration by authors 93
THE C-LOUD94
CALIFORNIAN IDEOLOGY-AYN RAND
Photo from the Internet ALL WATCHED OVER BY MACHINES OF LOVING GRACE
Russian-American writer and philosopher known developing philosopical system called objectivism
In 1995 The Californian ideology was founded by media theorists Richard Barbrook and Andy Cameron. The ideology tackles the effects of rise in networking technologies on society. This mythical idea suggests that newly develop computer and technologies can stablise the world without any hierarchy. Adam Curtis links the roots of Californian ideology to Ayn Rand, the objectivist. Her idea was a futuristic society where desires in stable world controlled by machines. This new body of governance would have destroyed our traditional centralized power and create a new kind of democracy. Everyone would be connected in a cybernetic network. Management of the C-loud is based on the promise of Californian ideology intelligence can lead the organization and decide on actions. It can observe and decide on the meaning and application of excessive surveillance depending on the site. Just as the algorithms of facial recognition systems trained as well. The project visions an ever-learning training for the c-loud that would happen through the analyses of the reactions given by the society. For C-loud everyone’s word is important. 95
THE C-LOUD
THE C-LOUD Proposal 96DESIGN
Number of the c-louds released depends on the size of the warning and would be determined by the main headquarter AI mechanism. When c-loud arrive to the location it will analyse the situation and the physical aspects of the environment. With its expanding skin it will cast a big enough shadow on the ground and release smoke drones to create an invisible bubble of smoke for the eclipse of surveillance to take place. When the invisible smoke is settled the c-loud will project fake architecture with lasers creating a holographic layer of counter surveillance. The sizes and directions of the projected walls will shift and change swiftly and randomly to minimize the risk of recording a footage from every angle of the face. The walls will create an extra layer on the face hence disabling the visibility of face to the cameras. When needs to reboot c-loud can rest on any building until being necessary again, restoring itself and sharing necessary data with the headquarters.
THE TheC-LOUDdesign proposes a new understanding to the regulation of face recognition surveillance through a network of organization. The new intergovernmental organization of surveillance is designated to observe and when necessary, act and eliminate or decrease the surveillance agents in a desired place. The project tries to level surveillance and counter surveillance through manipulation of surveillance since counter surveillance is mostly personal but facial recognition surveillance can be applied to masses.The proposed organization is an unmanned branch of United Nations, and it will be will be thought and trained by the local society. The main headquarters of the organization will be located in New York with UN headquarters while some mission launching stations will be located in desired locations. For today’s world proposed locations for mission launching stations are London, New York and Hong Kong since the local society is openly displeased by the excessive use of facial recognition surveillance technology. Even in these locations they can interpret the situation in any location, when necessary, very low energy demand like helium based iar ships. When excessive surveillance is observed by the warnings of the local society it will initiate a chain reaction. First the main headquarter in New York will be informed. Main headquarters will inform the closest mission launching station for
97 CONCEPTUAL COLLAGE Illustration by the authors
THE C-LOUD _UN_ ofIGO COUNTERSURVEILLANCE _LONDON HQ _NEW YORK HQ 98
THE C-LOUD _HONG KONG HQ _SHANGHAI HQ THE C-LOUD Proposed locations for c-loud launching stations and headquarter Illustration by authors 99
THE C-LOUD _UN_ ofIGO COUNTERSURVEILLANCE DETSEUQERNOITAVITCADUOL-C-1 Surveillance Surveillance Surveillance Surveillance Surveillance Surveillance Surveillance Surveillance 2-CLOSESTLAUNCHING STATION IS ACTIVATED 100
THE C-LOUD _SHANGHAI STATIONTVVLAUNCHING Surveillance Surveillance SurveillanceExcessive#nomorefacerecognition#myfacemydecision#stopusingmyface#banfacialrecognition#stopsurveillance#stopfacerecognition SurveillanceSurveillance SurveillanceSurveillanceSurveillance NIDUOL-C-3SSERGORP THE C-LOUD 1-When excessive surveillance is detected through surveillance sensors and observed reactions of the local society, main headquarter is warned. 2-Head quarter decides if action is required. If necessary closest launching station is warned location for regulation of surveillance Illustration by authors 101
THE C-LOUD _Surveillence sensors _LEARNING MECHANISM _DECISION MECHANISM _HEAD QUARTERS _Human Reactions and Behaviour _EXCESSIVE SURVEILLANCE 102
THE C-LOUD _THE _MECHANICAL_COMMUNICATION_COMPUTER/BRAINC-LOUDSECTIONSECTIONSECTION _Analysis of _Reflectenvironmenttheholographiccountersurveillance wall PROPOSED MECHANISM FOR REGULATION OF SURVEILLANCE Illustration by the authors 103
THE C-LOUD PRODUCED BY AN AUTODESK STUDENT VERSION VERSIONSTUDENTAUTODESKANBYPRODUCED PRODUCEDBYANAUTODESKSTUDENTVERSION VERSIONSTUDENTAUTODESKPRODUCEDBYAN 00:02:11:29 REC PRODUCED BY AN AUTODESK STUDENT VERSION VERSIONSTUDENTAUTODESKANBYPRODUCED PRODUCEDBYANAUTODESKSTUDENTVERSION VERSIONSTUDENTAUTODESKPRODUCEDBYAN 00:03:25:35 REC PRODUCED BY AN AUTODESK STUDENT VERSION VERSIONSTUDENTAUTODESKANBYPRODUCED PRODUCEDBYANAUTODESKSTUDENTVERSION VERSIONSTUDENTAUTODESKPRODUCEDBYAN 00:06:10:08 _expand REC PRODUCED BY AN AUTODESK STUDENT VERSION PRODUCED BY AN AUTODESK STUDENT VERSION VERSIONSTUDENTAUTODESKANBYPRODUCED PRODUCEDBYANAUTODESKSTUDENTVERSION VERSIONSTUDENTAUTODESKPRODUCEDBYAN 00:07:00:12 _drones REC PRODUCED BY AN AUTODESK STUDENT VERSION PRODUCED BY AN AUTODESK STUDENTPRODUCEDVERSIONBYANAUTODESKSTUDENTVERSION VERSIONSTUDENTAUTODESKANBYPRODUCED 00:07:50:20 _drones REC PRODUCED BY AN AUTODESK STUDENTPRODUCEDVERSIONBYANAUTODESKSTUDENTVERSION VERSIONSTUDENTAUTODESKANBYPRODUCED 00:15:28:36 REC PRODUCED BY AN AUTODESK STUDENTPRODUCEDVERSIONBYANAUTODESKSTUDENTVERSION VERSIONSTUDENTAUTODESKANBYPRODUCED 00:20:32:45 REC PRODUCED BY AN AUTODESK STUDENT VERSION VERSIONSTUDENTAUTODESKPRODUCEDBYAN VERSIONSTUDENTAUTODESKANBYPRODUCED PRODUCED BY AN AUTODESK STUDENTPRODUCEDVERSIONBYANAUTODESKSTUDENTVERSION VERSIONSTUDENTAUTODESKANBYPRODUCED 00:26:18:30 REC THE C-LOUD Steps of action for c-loud holographic wall face recognition counter surveillance Illustration by the authors 104
THE C-LOUD 105
FACE BEHIND THE
The holographic walls create an untouchable layer of protection on face for the protection from footage recording Illustration by the authors
WALL
THE C-LOUD THE ECLIPSE OF SURVEILLANCE C-loud uses its expanding skin to cast a shadow on the street. The size of the shadow can change according to the Illustration by the authors 106
THE C-LOUD THE INVISIBLE ZONE Drones released from the C-loud disperse an invisible layer of smoke to create a layer where the walls can be projected. Illustration by the authors 107
THE C-LOUD108 THE C-LOUD Interior structure of the C-loud Illustration by the 108Engine/computerCommunicationExpandingPVPropellerauthorsPanelsskinGas/HeliumstorageHeliumBalloonRoom/ServersroomPVPanelsWaterStorageBatteryZonePistonsDroneCorridorHolographicprojectors
THE C-LOUD THE C-LOUD Expanding skin structure for dynamic shadow Illustrationcatingbythe authors 109
THE C-LOUD110