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Apple’s FaceID

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.

More:

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.

More speculation.

I am not planning on enabling it just yet.

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Another iPhone Change to Frustrate the Police

I recently wrote about the new ability to disable the Touch ID login on iPhones. This is important because of a weirdness in current US law that protects people’s passcodes from forced disclosure in ways it does not protect actions: being forced to place a thumb on a fingerprint reader.

There’s another, more significant, change: iOS now requires a passcode before the phone will establish trust with another device.

In the current system, when you connect your phone to a computer, you’re prompted with the question “Trust this computer?” and you can click yes or no. Now you have to enter in your passcode again. That means if the police have an unlocked phone, they can scroll through the phone looking for things but they can’t download all of the contents onto a another computer without also knowing the passcode.

More details:

This might be particularly consequential during border searches. The “border search” exception, which allows Customs and Border Protection to search anything going into the country, is a contentious issue when applied electronics. It is somewhat (but not completely) settled law, but that the U.S. government can, without any cause at all (not even “reasonable articulable suspicion”, let alone “probable cause”), copy all the contents of my devices when I reenter the country sows deep discomfort in myself and many others. The only legal limitation appears to be a promise not to use this information to connect to remote services. The new iOS feature means that a Customs office can browse through a device — a time limited exercise — but not download the full contents.

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iOS 11 Allows Users to Disable Touch ID

A new feature in Apple’s new iPhone operating system — iOS 11 — will allow users to quickly disable Touch ID.

A new setting, designed to automate emergency services calls, lets iPhone users tap the power button quickly five times to call 911. This doesn’t automatically dial the emergency services by default, but it brings up the option to and also temporarily disables Touch ID until you enter a passcode.

This is useful in situations where the police cannot compel you to divulge your password, but can compel you to press your finger on the reader.

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Forging Voice

LyreBird is a system that can accurately reproduce the voice of someone, given a large amount of sample inputs. It’s pretty good — listen to the demo here — and will only get better over time.

The applications for recorded-voice forgeries are obvious, but I think the larger security risk will be real-time forgery. Imagine the social engineering implications of an attacker on the telephone being able to impersonate someone the victim knows.

I don’t think we’re ready for this. We use people’s voices to authenticate them all the time, in all sorts of different ways.

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Duress Codes for Fingerprint Access Control

Mike Specter has an interesting idea on how to make biometric access-control systems more secure: add a duress code. For example, you might configure your iPhone so that either thumb or forefinger unlocks the device, but your left middle finger disables the fingerprint mechanism (useful in the US where being compelled to divulge your password is a 5th Amendment violation but being forced to place your finger on the fingerprint reader is not) and the right middle finger permanently wipes the phone (useful in other countries where coercion techniques are much more severe).

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Heartbeat as Biometric Password

There’s research in using a heartbeat as a biometric password. No details in the article. My guess is that there isn’t nearly enough entropy in the reproducible biometric, but I might be surprised. The article’s suggestion to use it as a password for health records seems especially problematic. “I’m sorry, but we can’t access the patient’s health records because he’s having a heart attack.”

I wrote about this before here.

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Fooling Facial Recognition Systems

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.

News articles.

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Using Neural Networks to Identify Blurred Faces

Neural networks are good at identifying faces, even if they’re blurry:

In a paper released earlier this month, researchers at UT Austin and Cornell University demonstrate that faces and objects obscured by blurring, pixelation, and a recently-proposed privacy system called P3 can be successfully identified by a neural network trained on image datasets­ — in some cases at a more consistent rate than humans.

“We argue that humans may no longer be the ‘gold standard’ for extracting information from visual data,” the researchers write. “Recent advances in machine learning based on artificial neural networks have led to dramatic improvements in the state of the art for automated image recognition. Trained machine learning models now outperform humans on tasks such as object recognition and determining the geographic location of an image.”

Research paper

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