Two related stories:
PornHub is using machine learning algorithms to identify actors in different videos, so as to better index them. People are worried that it can really identify them, by linking their stage names to their real names.
Facebook somehow managed to link a sex worker’s clients under her fake name to her real profile.
Sometimes people have legitimate reasons for having two identities. That is becoming harder and harder.
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This is a good interview with Apple’s SVP of Software Engineering about FaceID.
Honestly, I don’t know what to think. I am confident that Apple is not collecting a photo database, but not optimistic that it can’t be hacked with fake faces. I dislike the fact that the police can point the phone at someone and have it automatically unlock. So this is important:
I also quizzed Federighi about the exact way you “quick disabled” Face ID in tricky scenarios — like being stopped by police, or being asked by a thief to hand over your device.
“On older phones the sequence was to click 5 times [on the power button], but on newer phones like iPhone 8 and iPhone X, if you grip the side buttons on either side and hold them a little while — we’ll take you to the power down [screen]. But that also has the effect of disabling Face ID,” says Federighi. “So, if you were in a case where the thief was asking to hand over your phone — you can just reach into your pocket, squeeze it, and it will disable Face ID. It will do the same thing on iPhone 8 to disable Touch ID.”
That squeeze can be of either volume button plus the power button. This, in my opinion, is an even better solution than the “5 clicks” because it’s less obtrusive. When you do this, it defaults back to your passcode.
It’s worth noting a few additional details here:
- If you haven’t used Face ID in 48 hours, or if you’ve just rebooted, it will ask for a passcode.
- If there are 5 failed attempts to Face ID, it will default back to passcode. (Federighi has confirmed that this is what happened in the demo onstage when he was asked for a passcode — it tried to read the people setting the phones up on the podium.)
- Developers do not have access to raw sensor data from the Face ID array. Instead, they’re given a depth map they can use for applications like the Snap face filters shown onstage. This can also be used in ARKit applications.
- You’ll also get a passcode request if you haven’t unlocked the phone using a passcode or at all in 6.5 days and if Face ID hasn’t unlocked it in 4 hours.
Also be prepared for your phone to immediately lock every time your sleep/wake button is pressed or it goes to sleep on its own. This is just like Touch ID.
Federighi also noted on our call that Apple would be releasing a security white paper on Face ID closer to the release of the iPhone X. So if you’re a researcher or security wonk looking for more, he says it will have “extreme levels of detail” about the security of the system.
Here’s more about fooling it with fake faces:
Facial recognition has long been notoriously easy to defeat. In 2009, for instance, security researchers showed that they could fool face-based login systems for a variety of laptops with nothing more than a printed photo of the laptop’s owner held in front of its camera. In 2015, Popular Science writer Dan Moren beat an Alibaba facial recognition system just by using a video that included himself blinking.
Hacking FaceID, though, won’t be nearly that simple. The new iPhone uses an infrared system Apple calls TrueDepth to project a grid of 30,000 invisible light dots onto the user’s face. An infrared camera then captures the distortion of that grid as the user rotates his or her head to map the face’s 3-D shape — a trick similar to the kind now used to capture actors’ faces to morph them into animated and digitally enhanced characters.
It’ll be harder, but I have no doubt that it will be done.
I am not planning on enabling it just yet.
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This is some interesting research. You can fool facial recognition systems by wearing glasses printed with elements of other peoples’ faces.
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K. Reiter, “Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition“:
ABSTRACT: Machine learning is enabling a myriad innovations, including new algorithms for cancer diagnosis and self-driving cars. The broad use of machine learning makes it important to understand the extent to which machine-learning algorithms are subject to attack, particularly when used in applications where physical security or safety is at risk. In this paper, we focus on facial biometric systems, which are widely used in surveillance and access control. We define and investigate a novel class of attacks: attacks that are physically realizable and inconspicuous, and allow an attacker to evade recognition or impersonate another individual. We develop a systematic method to automatically generate such attacks, which are realized through printing a pair of eyeglass frames. When worn by the attacker whose image is supplied to a state-of-the-art face-recognition algorithm, the eyeglasses allow her to evade being recognized or to impersonate another individual. Our investigation focuses on white-box face-recognition systems, but we also demonstrate how similar techniques can be used in black-box scenarios, as well as to avoid face detection.
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