The Wegman’s Supermarket Chain Is Probably Using Facial Recognition
The New York City Wegman’s is collecting biometric information about customers.
Entries Tagged "face recognition"
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Failures in Face Recognition
Interesting article on people with nonstandard faces and how facial recognition systems fail for them.
Some of those living with facial differences tell WIRED they have undergone multiple surgeries and experienced stigma for their entire lives, which is now being echoed by the technology they are forced to interact with. They say they haven’t been able to access public services due to facial verification services failing, while others have struggled to access financial services. Social media filters and face-unlocking systems on phones often won’t work, they say.
It’s easy to blame the tech, but the real issue are the engineers who only considered a narrow spectrum of potential faces. That needs to change. But also, we need easy-to-access backup systems when the primary ones fail.
Age Verification Using Facial Scans
Discord is testing the feature:
“We’re currently running tests in select regions to age-gate access to certain spaces or user settings,” a spokesperson for Discord said in a statement. “The information shared to power the age verification method is only used for the one-time age verification process and is not stored by Discord or our vendor. For Face Scan, the solution our vendor uses operates on-device, which means there is no collection of any biometric information when you scan your face. For ID verification, the scan of your ID is deleted upon verification.”
I look forward to all the videos of people hacking this system using various disguises.
Facial Scanning by Burger King in Brazil
In 2000, I wrote: “If McDonald’s offered three free Big Macs for a DNA sample, there would be lines around the block.”
Burger King in Brazil is almost there, offering discounts in exchange for a facial scan. From a marketing video:
“At the end of the year, it’s Friday every day, and the hangover kicks in,” a vaguely robotic voice says as images of cheeseburgers glitch in and out over fake computer code. “BK presents Hangover Whopper, a technology that scans your hangover level and offers a discount on the ideal combo to help combat it.” The stunt runs until January 2nd.
Facial Recognition Systems in the US
A helpful summary of which US retail stores are using facial recognition, thinking about using it, or currently not planning on using it. (This, of course, can all change without notice.)
Three years ago, I wrote that campaigns to ban facial recognition are too narrow. The problem here is identification, correlation, and then discrimination. There’s no difference whether the identification technology is facial recognition, the MAC address of our phones, gait recognition, license plate recognition, or anything else. Facial recognition is just the easiest technology right now.
On Technologies for Automatic Facial Recognition
Interesting article on technologies that will automatically identify people:
With technology like that on Mr. Leyvand’s head, Facebook could prevent users from ever forgetting a colleague’s name, give a reminder at a cocktail party that an acquaintance had kids to ask about or help find someone at a crowded conference. However, six years later, the company now known as Meta has not released a version of that product and Mr. Leyvand has departed for Apple to work on its Vision Pro augmented reality glasses.
The technology is here. Maybe the implementation is still dorky, but that will change. The social implications will be enormous.
Identity Theft from 1965 Uncovered through Face Recognition
Interesting story:
Napoleon Gonzalez, of Etna, assumed the identity of his brother in 1965, a quarter century after his sibling’s death as an infant, and used the stolen identity to obtain Social Security benefits under both identities, multiple passports and state identification cards, law enforcement officials said.
[…]
A new investigation was launched in 2020 after facial identification software indicated Gonzalez’s face was on two state identification cards.
The facial recognition technology is used by the Maine Bureau of Motor Vehicles to ensure no one obtains multiple credentials or credentials under someone else’s name, said Emily Cook, spokesperson for the secretary of state’s office.
Manipulating Weights in Face-Recognition AI Systems
Interesting research: “Facial Misrecognition Systems: Simple Weight Manipulations Force DNNs to Err Only on Specific Persons“:
Abstract: In this paper we describe how to plant novel types of backdoors in any facial recognition model based on the popular architecture of deep Siamese neural networks, by mathematically changing a small fraction of its weights (i.e., without using any additional training or optimization). These backdoors force the system to err only on specific persons which are preselected by the attacker. For example, we show how such a backdoored system can take any two images of a particular person and decide that they represent different persons (an anonymity attack), or take any two images of a particular pair of persons and decide that they represent the same person (a confusion attack), with almost no effect on the correctness of its decisions for other persons. Uniquely, we show that multiple backdoors can be independently installed by multiple attackers who may not be aware of each other’s existence with almost no interference.
We have experimentally verified the attacks on a FaceNet-based facial recognition system, which achieves SOTA accuracy on the standard LFW dataset of 99.35%. When we tried to individually anonymize ten celebrities, the network failed to recognize two of their images as being the same person in 96.97% to 98.29% of the time. When we tried to confuse between the extremely different looking Morgan Freeman and Scarlett Johansson, for example, their images were declared to be the same person in 91.51% of the time. For each type of backdoor, we sequentially installed multiple backdoors with minimal effect on the performance of each one (for example, anonymizing all ten celebrities on the same model reduced the success rate for each celebrity by no more than 0.91%). In all of our experiments, the benign accuracy of the network on other persons was degraded by no more than 0.48% (and in most cases, it remained above 99.30%).
It’s a weird attack. On the one hand, the attacker has access to the internals of the facial recognition system. On the other hand, this is a novel attack in that it manipulates internal weights to achieve a specific outcome. Given that we have no idea how those weights work, it’s an important result.
Recovering Real Faces from Face-Generation ML System
New paper: “This Person (Probably) Exists. Identity Membership Attacks Against GAN Generated Faces.
Abstract: Recently, generative adversarial networks (GANs) have achieved stunning realism, fooling even human observers. Indeed, the popular tongue-in-cheek website http://thispersondoesnotexist.com, taunts users with GAN generated images that seem too real to believe. On the other hand, GANs do leak information about their training data, as evidenced by membership attacks recently demonstrated in the literature. In this work, we challenge the assumption that GAN faces really are novel creations, by constructing a successful membership attack of a new kind. Unlike previous works, our attack can accurately discern samples sharing the same identity as training samples without being the same samples. We demonstrate the interest of our attack across several popular face datasets and GAN training procedures. Notably, we show that even in the presence of significant dataset diversity, an over represented person can pose a privacy concern.
Using “Master Faces” to Bypass Face-Recognition Authenticating Systems
Fascinating research: “Generating Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution.”
Abstract: A master face is a face image that passes face-based identity-authentication for a large portion of the population. These faces can be used to impersonate, with a high probability of success, any user, without having access to any user-information. We optimize these faces, by using an evolutionary algorithm in the latent embedding space of the StyleGAN face generator. Multiple evolutionary strategies are compared, and we propose a novel approach that employs a neural network in order to direct the search in the direction of promising samples, without adding fitness evaluations. The results we present demonstrate that it is possible to obtain a high coverage of the population (over 40%) with less than 10 master faces, for three leading deep face recognition systems.
Sidebar photo of Bruce Schneier by Joe MacInnis.