Entries Tagged "biometrics"

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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.

Posted on August 21, 2017 at 6:57 AM • View Comments

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.

EDITED TO ADD (5/11): This is from 2003 on the topic.

Posted on May 4, 2017 at 10:31 AM • View Comments

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).

Posted on January 26, 2017 at 2:03 PM • View Comments

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.

Posted on January 19, 2017 at 6:22 AM • View Comments

Fooling Facial Recognition Systems

This is some interesting research. You can fool facial recognition systems by wearing glasses printed with elements of other people’s 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.

Posted on November 11, 2016 at 7:31 AM • View Comments

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

Posted on September 27, 2016 at 9:39 AM • View Comments

Using Wi-Fi Signals to Identify People by Body Shape

Another paper on using Wi-Fi for surveillance. This one is on identifying people by their body shape. “FreeSense:Indoor Human Identification with WiFi Signals“:

Abstract: Human identification plays an important role in human-computer interaction. There have been numerous methods proposed for human identification (e.g., face recognition, gait recognition, fingerprint identification, etc.). While these methods could be very useful under different conditions, they also suffer from certain shortcomings (e.g., user privacy, sensing coverage range). In this paper, we propose a novel approach for human identification, which leverages WIFI signals to enable non-intrusive human identification in domestic environments. It is based on the observation that each person has specific influence patterns to the surrounding WIFI signal while moving indoors, regarding their body shape characteristics and motion patterns. The influence can be captured by the Channel State Information (CSI) time series of WIFI. Specifically, a combination of Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT) and Dynamic Time Warping (DTW) techniques is used for CSI waveform-based human identification. We implemented the system in a 6m*5m smart home environment and recruited 9 users for data collection and evaluation. Experimental results indicate that the identification accuracy is about 88.9% to 94.5% when the candidate user set changes from 6 to 2, showing that the proposed human identification method is effective in domestic environments.

EDITED TO ADD (9/13): Related paper.

Posted on August 30, 2016 at 12:57 PM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.