Entries Tagged "academic papers"

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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.

Posted on October 1, 2020 at 6:19 AMView Comments

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.

Posted on September 28, 2020 at 6:21 AMView Comments

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.

Posted on September 14, 2020 at 6:21 AMView Comments

Identifying People by Their Browsing Histories

Interesting paper: “Replication: Why We Still Can’t Browse in Peace: On the Uniqueness and Reidentifiability of Web Browsing Histories”:

We examine the threat to individuals’ privacy based on the feasibility of reidentifying users through distinctive profiles of their browsing history visible to websites and third parties. This work replicates and extends the 2012 paper Why Johnny Can’t Browse in Peace: On the Uniqueness of Web Browsing History Patterns[48]. The original work demonstrated that browsing profiles are highly distinctive and stable. We reproduce those results and extend the original work to detail the privacy risk posed by the aggregation of browsing histories. Our dataset consists of two weeks of browsing data from ~52,000 Firefox users. Our work replicates the original paper’s core findings by identifying 48,919 distinct browsing profiles, of which 99% are unique. High uniqueness hold seven when histories are truncated to just 100 top sites. We then find that for users who visited 50 or more distinct domains in the two-week data collection period, ~50% can be reidentified using the top 10k sites. Reidentifiability rose to over 80% for users that browsed 150 or more distinct domains. Finally, we observe numerous third parties pervasive enough to gather web histories sufficient to leverage browsing history as an identifier.

One of the authors of the original study comments on the replication.

Posted on August 25, 2020 at 6:28 AMView Comments

Using Disinformation to Cause a Blackout

Interesting paper: “How weaponizing disinformation can bring down a city’s power grid“:

Abstract: Social media has made it possible to manipulate the masses via disinformation and fake news at an unprecedented scale. This is particularly alarming from a security perspective, as humans have proven to be one of the weakest links when protecting critical infrastructure in general, and the power grid in particular. Here, we consider an attack in which an adversary attempts to manipulate the behavior of energy consumers by sending fake discount notifications encouraging them to shift their consumption into the peak-demand period. Using Greater London as a case study, we show that such disinformation can indeed lead to unwitting consumers synchronizing their energy-usage patterns, and result in blackouts on a city-scale if the grid is heavily loaded. We then conduct surveys to assess the propensity of people to follow-through on such notifications and forward them to their friends. This allows us to model how the disinformation may propagate through social networks, potentially amplifying the attack impact. These findings demonstrate that in an era when disinformation can be weaponized, system vulnerabilities arise not only from the hardware and software of critical infrastructure, but also from the behavior of the consumers.

I’m not sure the attack is practical, but it’s an interesting idea.

Posted on August 18, 2020 at 10:03 AMView Comments

UAE Hack and Leak Operations

Interesting paper on recent hack-and-leak operations attributed to the UAE:

Abstract: Four hack-and-leak operations in U.S. politics between 2016 and 2019, publicly attributed to the United Arab Emirates (UAE), Qatar, and Saudi Arabia, should be seen as the “simulation of scandal” ­– deliberate attempts to direct moral judgement against their target. Although “hacking” tools enable easy access to secret information, they are a double-edged sword, as their discovery means the scandal becomes about the hack itself, not about the hacked information. There are wider consequences for cyber competition in situations of constraint where both sides are strategic partners, as in the case of the United States and its allies in the Persian Gulf.

Posted on August 13, 2020 at 9:28 AMView Comments

Adversarial Machine Learning and the CFAA

I just co-authored a paper on the legal risks of doing machine learning research, given the current state of the Computer Fraud and Abuse Act:

Abstract: Adversarial Machine Learning is booming with ML researchers increasingly targeting commercial ML systems such as those used in Facebook, Tesla, Microsoft, IBM, Google to demonstrate vulnerabilities. In this paper, we ask, “What are the potential legal risks to adversarial ML researchers when they attack ML systems?” Studying or testing the security of any operational system potentially runs afoul the Computer Fraud and Abuse Act (CFAA), the primary United States federal statute that creates liability for hacking. We claim that Adversarial ML research is likely no different. Our analysis show that because there is a split in how CFAA is interpreted, aspects of adversarial ML attacks, such as model inversion, membership inference, model stealing, reprogramming the ML system and poisoning attacks, may be sanctioned in some jurisdictions and not penalized in others. We conclude with an analysis predicting how the US Supreme Court may resolve some present inconsistencies in the CFAA’s application in Van Buren v. United States, an appeal expected to be decided in 2021. We argue that the court is likely to adopt a narrow construction of the CFAA, and that this will actually lead to better adversarial ML security outcomes in the long term.

Medium post on the paper. News article, which uses our graphic without attribution.

Posted on July 23, 2020 at 6:03 AMView Comments

Fawkes: Digital Image Cloaking

Fawkes is a system for manipulating digital images so that they aren’t recognized by facial recognition systems.

At a high level, Fawkes takes your personal images, and makes tiny, pixel-level changes to them that are invisible to the human eye, in a process we call image cloaking. You can then use these “cloaked” photos as you normally would, sharing them on social media, sending them to friends, printing them or displaying them on digital devices, the same way you would any other photo. The difference, however, is that if and when someone tries to use these photos to build a facial recognition model, “cloaked” images will teach the model an highly distorted version of what makes you look like you. The cloak effect is not easily detectable, and will not cause errors in model training. However, when someone tries to identify you using an unaltered image of you (e.g. a photo taken in public), and tries to identify you, they will fail.

Research paper.

EDITED TO ADD (8/3): Kashmir Hill checks it out, and it’s got problems.

Another article.

Posted on July 22, 2020 at 9:12 AMView Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.