Smartphone Photos Fight Cybercrime
January 3, 2018
Not comfortable with Face ID and other biometrics? This
cybersecurity advancement may be for you.
A University at Buffalo-led team of researchers has discovered
how to identify smartphones by examining just one photo taken by
the device. The advancement opens the possibility of using
smartphones -- instead of body parts -- as a form of
identification to deter cybercrime.
"Like snowflakes, no two smartphones are the same. Each device,
regardless of the manufacturer or make, can be identified
through a pattern of microscopic imaging flaws that are present
in every picture they take," says Kui Ren, the study's lead
author. "It's kind of like matching bullets to a gun, only we're
matching photos to a smartphone camera."
Like bullets fired from a gun, photos can be traced to
individual smartphones, opening up new ways to prevent identity
The new technology, to be presented in February at the 2018
Network and Distributed Systems Security Conference in
California, is not yet available to the public. However, it
could become part of the authentication process -- like PIN
numbers and passwords -- that customers complete at cash
registers, ATMs and during online transactions.
For people who've had their personal identification stolen, it
could also help prevent cybercriminals from using that
information to make purchases in their name, says Ren, PhD, SUNY
Empire Innovation Professor in the Department of Computer
Science and Engineering in UB's School of Engineering and
How each camera is unique
The study -- "ABC: Enabling Smartphone Authentication with
Built-in Camera" -- centers on an obscure flaw in digital
imaging called photo-response non-uniformity (PRNU).
Digital cameras are built to be identical. However,
manufacturing imperfections create tiny variations in each
camera's sensors. These variations can cause some of sensors'
millions of pixels to project colors that are slightly brighter
or darker than they should be.
Not visible to the naked eye, this lack of uniformity forms a
systemic distortion in the photo called pattern noise. Extracted
by special filters, the pattern is unique for each camera.
First observed in conventional digital cameras, PRNU analysis is
common in digital forensic science. For example, it can help
settle copyright lawsuits involving photographs.
But it hasn't been applied to cybersecurity -- despite the
ubiquity of smartphones -- because extracting it had required
analyzing 50 photos taken by a camera, and experts though that
customers wouldn't be willing to supply that many photos. Plus,
savvy cybercriminals can fake the pattern by analyzing images
taken with a smartphone that victims post on unsecured websites.
Applying the technique to cybersecurity
The study addresses how each of these challenges can be
Compared to a conventional digital camera, the image sensor of a
smartphone is much smaller. The reduction amplifies the pixels'
dimensional non-uniformity and generates a much stronger PRNU.
As a result, it's possible to match a photo to a smartphone
camera using one photo instead of the 50 normally required for
"I think most people assumed you would need 50 images to
identify a smartphone camera. But our research shows that's not
the case," says Ren, an IEEE (Institute of Electrical and
Electronics Engineers) Fellow and an ACM (Association for
Computing Machinery) Distinguished Scientist.
To prevent forgeries, Ren designed a protocol -- it is part of
the authentication process described below -- which detects and
stops two types of attacks.
How the new security protocol works
The study discusses how such a system might work. First, a
customer registers with a business -- such as a bank or retailer
-- and provides that business with a photo that serves as a
When a customer initiates a transaction, the retailer asks the
customer (likely through an app) to photograph two QR codes (a
type of barcode that contains information about the transaction)
presented on an ATM, cash register or other screen.
Using the app, the customer then sends the photograph back to
the retailer, which scans the picture to measure the
smartphone's PRNU. The retailer can detect a forgery because the
PRNU of the attacker's camera will alter the PRNU component of
More savvy cybercriminals could potentially remove the PRNU from
their device. But Ren's protocol can spot this because the QR
codes include an embedded probe signal that will be weakened by
the removal process.
transaction is either approved or denied based upon these tests.
Results and what's next
The protocol defeats three of the most common tactics used by
cybercriminals: fingerprint forgery attacks, man-in-the-middle
attacks and replay attacks. It was 99.5 percent accurate in
tests involving 16,000 images and 30 different iPhone 6s
smartphones and 10 different Galaxy Note 5s smartphones.
Ren plans to lead future experiments on smartphones that include
two cameras, which he said could be used to make the forgery
attacks more difficult.