Split-Second Phantom Images Fool Autopilots

Split-Second Phantom Images Fool Autopilots

Researchers are tricking autopilots by inserting split-second images into roadside billboards.

Researchers at Israel’s Ben Gurion University of the Negev … previously revealed that they could use split-second light projections on roads to successfully trick Tesla’s driver-assistance systems into automatically stopping without warning when its camera sees spoofed images of road signs or pedestrians. In new research, they’ve found they can pull off the same trick with just a few frames of a road sign injected on a billboard’s video. And they warn that if hackers hijacked an internet-connected billboard to carry out the trick, it could be used to cause traffic jams or even road accidents while leaving little evidence behind.


In this latest set of experiments, the researchers injected frames of a phantom stop sign on digital billboards, simulating what they describe as a scenario in which someone hacked into a roadside billboard to alter its video. They also upgraded to Tesla’s most recent version of Autopilot known as HW3. They found that they could again trick a Tesla or cause the same Mobileye device to give the driver mistaken alerts with just a few frames of altered video.

The researchers found that an image that appeared for 0.42 seconds would reliably trick the Tesla, while one that appeared for just an eighth of a second would fool the Mobileye device. They also experimented with finding spots in a video frame that would attract the least notice from a human eye, going so far as to develop their own algorithm for identifying key blocks of pixels in an image so that a half-second phantom road sign could be slipped into the “uninteresting” portions.

The paper:

Abstract: In this paper, we investigate “split-second phantom attacks,” a scientific gap that causes two commercial advanced driver-assistance systems (ADASs), Telsa Model X (HW 2.5 and HW 3) and Mobileye 630, to treat a depthless object that appears for a few milliseconds as a real obstacle/object. We discuss the challenge that split-second phantom attacks create for ADASs. We demonstrate how attackers can apply split-second phantom attacks remotely by embedding phantom road signs into an advertisement presented on a digital billboard which causes Tesla’s autopilot to suddenly stop the car in the middle of a road and Mobileye 630 to issue false notifications. We also demonstrate how attackers can use a projector in order to cause Tesla’s autopilot to apply the brakes in response to a phantom of a pedestrian that was projected on the road and Mobileye 630 to issue false notifications in response to a projected road sign. To counter this threat, we propose a countermeasure which can determine whether a detected object is a phantom or real using just the camera sensor. The countermeasure (GhostBusters) uses a “committee of experts” approach and combines the results obtained from four lightweight deep convolutional neural networks that assess the authenticity of an object based on the object’s light, context, surface, and depth. We demonstrate our countermeasure’s effectiveness (it obtains a TPR of 0.994 with an FPR of zero) and test its robustness to adversarial machine learning attacks.

Sidebar photo of Bruce Schneier by Joe MacInnis.

On Risk-Based Authentication

On Risk-Based Authentication

Interesting usability study: “More Than Just Good Passwords? A Study on Usability and Security Perceptions of Risk-based Authentication“:

Abstract: Risk-based Authentication (RBA) is an adaptive security measure to strengthen password-based authentication. RBA monitors additional features during login, and when observed feature values differ significantly from previously seen ones, users have to provide additional authentication factors such as a verification code. RBA has the potential to offer more usable authentication, but the usability and the security perceptions of RBA are not studied well.

We present the results of a between-group lab study (n=65) to evaluate usability and security perceptions of two RBA variants, one 2FA variant, and password-only authentication. Our study shows with significant results that RBA is considered to be more usable than the studied 2FA variants, while it is perceived as more secure than password-only authentication in general and comparably se-cure to 2FA in a variety of application types. We also observed RBA usability problems and provide recommendations for mitigation.Our contribution provides a first deeper understanding of the users’perception of RBA and helps to improve RBA implementations for a broader user acceptance.

Paper’s website. I’ve blogged about risk-based authentication before.

Sidebar photo of Bruce Schneier by Joe MacInnis.

Detecting Deep Fakes with a Heartbeat

Detecting Deep Fakes with a Heartbeat

Researchers can detect deep fakes because they don’t convincingly mimic human blood circulation in the face:

In particular, video of a person’s face contains subtle shifts in color that result from pulses in blood circulation. You might imagine that these changes would be too minute to detect merely from a video, but viewing videos that have been enhanced to exaggerate these color shifts will quickly disabuse you of that notion. This phenomenon forms the basis of a technique called photoplethysmography, or PPG for short, which can be used, for example, to monitor newborns without having to attach anything to a their very sensitive skin.

Deep fakes don’t lack such circulation-induced shifts in color, but they don’t recreate them with high fidelity. The researchers at SUNY and Intel found that “biological signals are not coherently preserved in different synthetic facial parts” and that “synthetic content does not contain frames with stable PPG.” Translation: Deep fakes can’t convincingly mimic how your pulse shows up in your face.

The inconsistencies in PPG signals found in deep fakes provided these researchers with the basis for a deep-learning system of their own, dubbed FakeCatcher, which can categorize videos of a person’s face as either real or fake with greater than 90 percent accuracy. And these same three researchers followed this study with another demonstrating that this approach can be applied not only to revealing that a video is fake, but also to show what software was used to create it.

Of course, this is an arms race. I expect deep fake programs to become good enough to fool FakeCatcher in a few months.

Sidebar photo of Bruce Schneier by Joe MacInnis.

On Executive Order 12333

On Executive Order 12333

Mark Jaycox has written a long article on the US Executive Order 12333: “No Oversight, No Limits, No Worries: A Primer on Presidential Spying and Executive Order 12,333“:

Abstract: Executive Order 12,333 (“EO 12333”) is a 1980s Executive Order signed by President Ronald Reagan that, among other things, establishes an overarching policy framework for the Executive Branch’s spying powers. Although electronic surveillance programs authorized by EO 12333 generally target foreign intelligence from foreign targets, its permissive targeting standards allow for the substantial collection of Americans’ communications containing little to no foreign intelligence value. This fact alone necessitates closer inspection.

This working draft conducts such an inspection by collecting and coalescing the various declassifications, disclosures, legislative investigations, and news reports concerning EO 12333 electronic surveillance programs in order to provide a better understanding of how the Executive Branch implements the order and the surveillance programs it authorizes. The Article pays particular attention to EO 12333’s designation of the National Security Agency as primarily responsible for conducting signals intelligence, which includes the installation of malware, the analysis of internet traffic traversing the telecommunications backbone, the hacking of U.S.-based companies like Yahoo and Google, and the analysis of Americans’ communications, contact lists, text messages, geolocation data, and other information.

After exploring the electronic surveillance programs authorized by EO 12333, this Article proposes reforms to the existing policy framework, including narrowing the aperture of authorized surveillance, increasing privacy standards for the retention of data, and requiring greater transparency and accountability.

Interesting Attack on the EMV Smartcard Payment Standard

It’s complicated, but it’s basically a man-in-the-middle attack that involves two smartphones. The first phone reads the actual smartcard, and then forwards the required information to a second phone. That second phone actually conducts the transaction on the POS terminal. That second phone is able to convince the POS terminal to conduct the transaction without requiring the normally required PIN.

From a news article:

The researchers were able to demonstrate that it is possible to exploit the vulnerability in practice, although it is a fairly complex process. They first developed an Android app and installed it on two NFC-enabled mobile phones. This allowed the two devices to read data from the credit card chip and exchange information with payment terminals. Incidentally, the researchers did not have to bypass any special security features in the Android operating system to install the app.

To obtain unauthorized funds from a third-party credit card, the first mobile phone is used to scan the necessary data from the credit card and transfer it to the second phone. The second phone is then used to simultaneously debit the amount at the checkout, as many cardholders do nowadays. As the app declares that the customer is the authorized user of the credit card, the vendor does not realize that the transaction is fraudulent. The crucial factor is that the app outsmarts the card’s security system. Although the amount is over the limit and requires PIN verification, no code is requested.

The paper: “The EMV Standard: Break, Fix, Verify.”

Abstract: EMV is the international protocol standard for smartcard payment and is used in over 9 billion cards worldwide. Despite the standard’s advertised security, various issues have been previously uncovered, deriving from logical flaws that are hard to spot in EMV’s lengthy and complex specification, running over 2,000 pages.

We formalize a comprehensive symbolic model of EMV in Tamarin, a state-of-the-art protocol verifier. Our model is the first that supports a fine-grained analysis of all relevant security guarantees that EMV is intended to offer. We use our model to automatically identify flaws that lead to two critical attacks: one that defrauds the cardholder and another that defrauds the merchant. First, criminals can use a victim’s Visa contact-less card for high-value purchases, without knowledge of the card’s PIN. We built a proof-of-concept Android application and successfully demonstrated this attack on real-world payment terminals. Second, criminals can trick the terminal into accepting an unauthentic offline transaction, which the issuing bank should later decline, after the criminal has walked away with the goods. This attack is possible for implementations following the standard, although we did not test it on actual terminals for ethical reasons. Finally, we propose and verify improvements to the standard that prevent these attacks, as well as any other attacks that violate the considered security properties.The proposed improvements can be easily implemented in the terminals and do not affect the cards in circulation.