Veda Adnani, Nick Alexander, Amreen Ashraf
Our response includes slide deck linked here.
We examined the field of countering computer vision (with a focus on face detection), began to speculate on further developments, and consider research and design projects.
For our research on computer vision, we used a top-down approach. We started out trying to understand what “computer vision” is and what its implications are. Computer vision is the name given to a series of technologies which help the way a computer sees. The human eye is important to the way we use our visual understanding of the world to piece together information, in the same way, the camera is the eye of the computational device.
As of 2019, computer vision is all around us. Our smartphones, apps, social media, banks and other industries, use computer vision every day in aiding humans to carry out tasks with computational devices.
In Class Activity:
We started out by doing the class activity which was to research our topic. Some of the apps we looked at were those used commercially like the newly acquired app “…” by L’oreal. We also looked at the list “faces in new media” which is a list by Kyle Mcdonald (Face in new media art Kyle Mcdonald). The list is comprised of artists using computer vision in new and novel ways. In this list, there is a section on intervention which highlights arts using computer vision to counter tracking and thereby subverting these technologies.
We conducted a broad range of research to understand face tracking used by industries and governments to not just collect data but to classify humans and other potential uses of computer vision. Some initial concepts we jotted down were:
- Deepface: using facial recognition AI algorithms to alert or highlight when being detected.
- Blockchain: using blockchain technologies to scramble and save data on different databases for security.
- Physical: Using physical objects or clothing to misdirect.
Some interesting things we came across in this phase of the research was the way governments across the world are using computer vision. Privacy international is an NGO that does a lot of work with the legality of the ways in which computer vision is currently being implemented.
Instagram Face Filters:
Our first and most basic experiment was experimenting with Instagram face filters to understand the extent to which they can be used to alter, modify or even transform the face. One of the most striking filters that we found is shown below. It is called “Face Patch” and it gradually eliminates all the features from the user’s face leaving them only with a blank patch of skin and the outline of their head. We leave this finding open to your interpretation.
Beating Apple’s Facial Recognition
We tried deceiving Apple’s “True Depth” Face ID by using photographs, however this did not work. What did work was when we tried using a mirror to detect the face, and we found this odd since a mirror is a flat surface and cannot convey depth. Yet it somehow managed to cheat the software and unlock the device.
We experimented with Modiface an AR app that uses facial recognition to mockup different cosmetic products on the wearer’s face. A range of brands like INGLOT use this platform to advertise their products but what caught our attention was the apps ability to remove any scars and blemishes on the user’s face, even ones that the user was unaware of. It also allowed the user to change their eye colour if they desired. This was quite disturbing, and a rude awakening into the lengths that the beauty and cosmetics industry goes to, to promote vanity and unrealistic aesthetic perfection.
Free and Accessible Resources:
Accessible and free sources for body tracking are easy to find. Simple but robust face tracker tools made by independent developers, like CLM Face Tracker or Tracking.js are available with a minimum of web searching. More robust face tracking technology, such as that developed by Intel and Microsoft, is easily accessible by businesses. Body tracking code such as Posenet can also be found very easily.
For those who care to look, face and body tracking is widely available and can be adapted to a user’s purpose with no oversight.
Deceive v/s Defeat:
Through our research process, we came across two possible scenarios to subvert face recognition products. The first one was to “deceive” the intelligence into thinking that the user was someone else, and the second one was to “defeat” the system by rendering the user unidentifiable using certain tactics. Our findings below cover both of these possibilities.
In light of the examples listed below, we see an emerging need for subversion. Our identities, faces and bodies are sacred and personal. But we are constantly being violated by multiple entities, and it is unfair to be subjected to this kind of surveillance unknowingly. Where does this impending lack of trust leave humankind?
Amazon claims “ “Real-time face recognition across tens of millions of faces and detection of up to 100 faces in challenging crowded photos.” And was recently caught secretly licensing this facial recognition software to multiple state governments in the USA. With is real-time tracking and the ability to analyze several camera-feeds in multiple cities simultaneously, this is a serious concern for privacy and consent with government surveillance entities.
Using the same technology provided by Amazon’s recognition. Butterfleye is a B2C facial recognition device, that was built to help businesses get to “know their customers better”. Every time a customer enters any business establish like a coffee shop/salon/bank, the person serving the customer is immediately given a bank of data including the customers personal details, preferences and purchase history. They claim its a way for businesses to become more “efficient” and serve customers better, but where does this leave any possibility of privacy for the average human being?
SenseTime: Viper Surveillance System
SenseTime is a Chinese company that focuses on AI-based facial recognition systems. It is currently the most highly valued entity of its kind in the world at a net worth of 3 BILLION dollars. It’s flagship product the Viper Surveillance System detects faces in crowded areas and is most used by the government. What is shocking is that the government uses this technology the most in provinces with dense Muslim populations to track “terrorist” activity. However, its claims for doing so are far different.
Government claims across the globe:
Most governments are employing facial recognition software for various reasons. Some claim it is to find missing children, others claim it is to prevent and stop human trafficking. However, the actual uses are far from the truth they project.
AI-Generated Human Faces
AI-assisted image editing is used in the creation of “deepfakes” (a portmanteau of “deep learning” and “fake”) which are high-quality superimpositions of faces onto bodies. Generative Adversarial Networks have also been used to generate high-quality human faces, which, using face tracking technology, can be made to seem to be speaking in real-time.
Video forensics can be used, or image metadata can be extracted and analyzed, to identify AI-generated faces and videos.
How does one evade these various entities?
Classifiers v/s Detectors:
One of the key differences in surveillance systems is that between classifiers and detectors. While classifiers work towards categorizing pre-determined objects and are commonly used in face surveillance systems such as Apple’s True Depth Face ID with 30,000 touch points to identify faces. Detectors have to locate and determine objects themselves, i.e. create their own bounding boxes and are used in areas like autonomous driving vehicles.
NSAF: Hyphen Labs
Hyphen Labs is a multidisciplinary lab which focuses on using technological tools to empower women of colour. They use human-centred design and speculative design methodologies in the aid of prototyping technologies. They have developed a concept called Neurospeculative Afrofuturism which integrates computational technologies, virtual reality and neuroscience to aid in the design of prototypes. HyperFace is a prototype which uses many faces drawn onto a scarf to misdirect the use of computer vision in data collection and profiling. It uses the data points used by tracking software to graphically design a scarf which has many of these points. It also uses certain colours which are not recognized by this software.
Glasses that confuse surveillance:
Researchers at Carnegie Melon University have devised a pair of glasses that “perturb” or confuse facial recognition systems
Facial Camouflage that disturbs surveillance:
A team of researchers at Standford U led by Dr. Jiajun Lu have devised facial camouflage patterns to confuse cameras. This pattern renders the face unidentifiable from various angles, distances, lighting and so sob. They are experimenting with “living tattoos” for the face to create long term solutions to fight surveillance.
NIR LED Glasses, Caps or Burqa:
A low cost and feasible way to avoid any facial surveillance system is using Near Infrared LED lights. The lights are practically invisible to the naked human eye and when designed well in a prototype they can go unnoticed. The lights successfully blind cameras. The first prototype was a pair of eyeglasses designed by professors Isao Echizen and Seiichi Gohshi of Kogakuin University, and since then various prototypes ranging from caps to burqas have been made. The lights are inexpensive and available on Sparkfun.
THe URME mask is a 400$ mask sold at cost by its founder to help people evade surveillance. When worn, it is extremely realistic and the only time a wearer can be detected is when the lack of lip movement is noticed.
Facial Weaponization suite:
Facial Weaponization is a series of modelled masks created in revolt to the political spectrum of facial surveillance. The masks are made in workshops using aggregated data from participants that are unrecognizable by biometric facial surveillance systems.
In addition to exploring existing forms of facial countermeasures (like CV Dazzle) we considered utilizing the technology against itself. We imagined a digital mask that superimposed itself over any image recognized as a face taken by a device it was installed on, scrambling it and rendering it useless for facial data. We also considered bio-powered Near-Infared LED stickers that could be placed subtly on a face, and powered by body electricity.