“Markpainting” is a clever technique to watermark photos in such a way that makes it easier to detect ML-based manipulation:
An image owner can modify their image in subtle ways which are not themselves very visible, but will sabotage any attempt to inpaint it by adding visible information determined in advance by the markpainter.
One application is tamper-resistant marks. For example, a photo agency that makes stock photos available on its website with copyright watermarks can markpaint them in such a way that anyone using common editing software to remove a watermark will fail; the copyright mark will be markpainted right back. So watermarks can be made a lot more robust.
Here’s the paper: “Markpainting: Adversarial Machine Learning Meets Inpainting,” by David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, and Ross Anderson.
Abstract: Inpainting is a learned interpolation technique that is based on generative modeling and used to populate masked or missing pieces in an image; it has wide applications in picture editing and retouching. Recently, inpainting started being used for watermark removal, raising concerns. In this paper we study how to manipulate it using our markpainting technique. First, we show how an image owner with access to an inpainting model can augment their image in such a way that any attempt to edit it using that model will add arbitrary visible information. We find that we can target multiple different models simultaneously with our technique. This can be designed to reconstitute a watermark if the editor had been trying to remove it. Second, we show that our markpainting technique is transferable to models that have different architectures or were trained on different datasets, so watermarks created using it are difficult for adversaries to remove. Markpainting is novel and can be used as a manipulation alarm that becomes visible in the event of inpainting.
Posted on June 10, 2021 at 6:19 AM •
Henry Farrell and I published a paper on fixing American democracy: “Rechanneling Beliefs: How Information Flows Hinder or Help Democracy.”
It’s much easier for democratic stability to break down than most people realize, but this doesn’t mean we must despair over the future. It’s possible, though very difficult, to back away from our current situation towards one of greater democratic stability. This wouldn’t entail a restoration of a previous status quo. Instead, it would recognize that the status quo was less stable than it seemed, and a major source of the tensions that have started to unravel it. What we need is a dynamic stability, one that incorporates new forces into American democracy rather than trying to deny or quash them.
This paper is our attempt to explain what this might mean in practice. We start by analyzing the problem and explaining more precisely why a breakdown in public consensus harms democracy. We then look at how these beliefs are being undermined by three feedback loops, in which anti-democratic actions and anti-democratic beliefs feed on each other. Finally, we explain how these feedback loops might be redirected so as to sustain democracy rather than undermining it.
To be clear: redirecting these and other energies in more constructive ways presents enormous challenges, and any plausible success will at best be untidy and provisional. But, almost by definition, that’s true of any successful democratic reforms where people of different beliefs and values need to figure out how to coexist. Even when it’s working well, democracy is messy. Solutions to democratic breakdowns are going to be messy as well.
This is part of our series of papers looking at democracy as an information system. The first paper was “Common-Knowledge Attacks on Democracy.”
Posted on June 9, 2021 at 6:46 AM •
Interesting research paper.
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
Read my blog posting guidelines here.
Posted on May 28, 2021 at 4:09 PM •
There’s new research that demonstrates security vulnerabilities in all of the AMD and Intel chips with micro-op caches, including the ones that were specifically engineered to be resistant to the Spectre/Meltdown attacks of three years ago.
The new line of attacks exploits the micro-op cache: an on-chip structure that speeds up computing by storing simple commands and allowing the processor to fetch them quickly and early in the speculative execution process, as the team explains in a writeup from the University of Virginia. Even though the processor quickly realizes its mistake and does a U-turn to go down the right path, attackers can get at the private data while the processor is still heading in the wrong direction.
It seems really difficult to exploit these vulnerabilities. We’ll need some more analysis before we understand what we have to patch and how.
Posted on May 5, 2021 at 10:35 AM •
The new 802.11bf standard will turn Wi-Fi devices into object sensors:
In three years or so, the Wi-Fi specification is scheduled to get an upgrade that will turn wireless devices into sensors capable of gathering data about the people and objects bathed in their signals.
“When 802.11bf will be finalized and introduced as an IEEE standard in September 2024, Wi-Fi will cease to be a communication-only standard and will legitimately become a full-fledged sensing paradigm,” explains Francesco Restuccia, assistant professor of electrical and computer engineering at Northeastern University, in a paper summarizing the state of the Wi-Fi Sensing project (SENS) currently being developed by the Institute of Electrical and Electronics Engineers (IEEE).
SENS is envisioned as a way for devices capable of sending and receiving wireless data to use Wi-Fi signal interference differences to measure the range, velocity, direction, motion, presence, and proximity of people and objects.
More detail in the article. Security and privacy controls are still to be worked out, which means that there probably won’t be any.
Posted on April 5, 2021 at 6:15 AM •
It’s not yet very accurate or practical, but under ideal conditions it is possible to figure out the shape of a house key by listening to it being used.
Listen to Your Key: Towards Acoustics-based Physical Key Inference
Abstract: Physical locks are one of the most prevalent mechanisms for securing objects such as doors. While many of these locks are vulnerable to lock-picking, they are still widely used as lock-picking requires specific training with tailored instruments, and easily raises suspicion. In this paper, we propose SpiKey, a novel attack that significantly lowers the bar for an attacker as opposed to the lock-picking attack, by requiring only the use of a smartphone microphone to infer the shape of victim’s key, namely bittings(or cut depths) which form the secret of a key. When a victim inserts his/her key into the lock, the emitted sound is captured by the attacker’s microphone.SpiKey leverages the time difference between audible clicks to ultimately infer the bitting information, i.e., shape of the physical key. As a proof-of-concept, we provide a simulation, based on real-world recordings, and demonstrate a significant reduction in search spacefrom a pool of more than 330 thousand keys to three candidate keys for the most frequent case.
Scientific American podcast:
The strategy is a long way from being viable in the real world. For one thing, the method relies on the key being inserted at a constant speed. And the audio element also poses challenges like background noise.
Boing Boing post.
EDITED TO ADD (4/14): I seem to have blogged this previously.
Posted on March 24, 2021 at 6:10 AM •
Interesting research: “Who Can Find My Devices? Security and Privacy of Apple’s Crowd-Sourced Bluetooth Location Tracking System“:
Abstract: Overnight, Apple has turned its hundreds-of-million-device ecosystem into the world’s largest crowd-sourced location tracking network called offline finding (OF). OF leverages online finder devices to detect the presence of missing offline devices using Bluetooth and report an approximate location back to the owner via the Internet. While OF is not the first system of its kind, it is the first to commit to strong privacy goals. In particular, OF aims to ensure finder anonymity, untrackability of owner devices, and confidentiality of location reports. This paper presents the first comprehensive security and privacy analysis of OF. To this end, we recover the specifications of the closed-source OF protocols by means of reverse engineering. We experimentally show that unauthorized access to the location reports allows for accurate device tracking and retrieving a user’s top locations with an error in the order of 10 meters in urban areas. While we find that OF’s design achieves its privacy goals, we discover two distinct design and implementation flaws that can lead to a location correlation attack and unauthorized access to the location history of the past seven days, which could deanonymize users. Apple has partially addressed the issues following our responsible disclosure. Finally, we make our research artifacts publicly available.
There is also code available on GitHub, which allows arbitrary Bluetooth devices to be tracked via Apple’s Find My network.
Posted on March 15, 2021 at 6:16 AM •
Really interesting research:
“Exploitation and Sanitization of Hidden Data in PDF Files”
Abstract: Organizations publish and share more and more electronic documents like PDF files. Unfortunately, most organizations are unaware that these documents can compromise sensitive information like authors names, details on the information system and architecture. All these information can be exploited easily by attackers to footprint and later attack an organization. In this paper, we analyze hidden data found in the PDF files published by an organization. We gathered a corpus of 39664 PDF files published by 75 security agencies from 47 countries. We have been able to measure the quality and quantity of information exposed in these PDF files. It can be effectively used to find weak links in an organization: employees who are running outdated software. We have also measured the adoption of PDF files sanitization by security agencies. We identified only 7 security agencies which sanitize few of their PDF files before publishing. Unfortunately, we were still able to find sensitive information within 65% of these sanitized PDF files. Some agencies are using weak sanitization techniques: it requires to remove all the hidden sensitive information from the file and not just to remove the data at the surface. Security agencies need to change their sanitization methods.
Short summary: no one is doing great.
Posted on March 12, 2021 at 6:03 AM •
Science has a paper (and commentary) on generating 250 random terabits per second with a laser. I don’t know how cryptographically secure they are, but that can be cleaned up with something like Fortuna.
EDITED TO ADD (3/12): Here are free versions of the paper and the commentary.
Posted on March 11, 2021 at 6:15 AM •
Interesting paper: “Shadow Attacks: Hiding and Replacing Content in Signed PDFs“:
Abstract: Digitally signed PDFs are used in contracts and invoices to guarantee the authenticity and integrity of their content. A user opening a signed PDF expects to see a warning in case of any modification. In 2019, Mladenov et al. revealed various parsing vulnerabilities in PDF viewer implementations.They showed attacks that could modify PDF documents without invalidating the signature. As a consequence, affected vendors of PDF viewers implemented countermeasures preventing all attacks.
This paper introduces a novel class of attacks, which we call shadow attacks. The shadow attacks circumvent all existing countermeasures and break the integrity protection of digitally signed PDFs. Compared to previous attacks, the shadow attacks do not abuse implementation issues in a PDF viewer. In contrast, shadow attacks use the enormous flexibility provided by the PDF specification so that shadow documents remain standard-compliant. Since shadow attacks abuse only legitimate features,they are hard to mitigate.
Our results reveal that 16 (including Adobe Acrobat and Foxit Reader) of the 29 PDF viewers tested were vulnerable to shadow attacks. We introduce our tool PDF-Attacker which can automatically generate shadow attacks. In addition, we implemented PDF-Detector to prevent shadow documents from being signed or forensically detect exploits after being applied to signed PDFs.
EDITED TO ADD (3/12): This was written about last summer.
Posted on March 8, 2021 at 6:10 AM •
Sidebar photo of Bruce Schneier by Joe MacInnis.