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.
Sound waves through the body are unique enough to be a biometric:
“Modeling allowed us to infer what structures or material features of the human body actually differentiated people,” explains Joo Yong Sim, one of the ETRI researchers who conducted the study. “For example, we could see how the structure, size, and weight of the bones, as well as the stiffness of the joints, affect the bioacoustics spectrum.”
Notably, the researchers were concerned that the accuracy of this approach could diminish with time, since the human body constantly changes its cells, matrices, and fluid content. To account for this, they acquired the acoustic data of participants at three separate intervals, each 30 days apart.
“We were very surprised that people’s bioacoustics spectral pattern maintained well over time, despite the concern that the pattern would change greatly,” says Sim. “These results suggest that the bioacoustics signature reflects more anatomical features than changes in water, body temperature, or biomolecule concentration in blood that change from day to day.”
It’s not great. A 97% accuracy is worse than fingerprints and iris scans, and while they were able to reproduce the biometric in a month it almost certainly changes as we age, gain and lose weight, and so on. Still, interesting.
You can sign on as an individual or an organization. I did. You should as well. No, I don’t think that countries will magically adopt this moratorium. But it’s important for us all to register our dissent.
Excellent op-ed on the growing trend to tie humanitarian aid to surveillance.
Despite the best intentions, the decision to deploy technology like biometrics is built on a number of unproven assumptions, such as, technology solutions can fix deeply embedded political problems. And that auditing for fraud requires entire populations to be tracked using their personal data. And that experimental technologies will work as planned in a chaotic conflict setting. And last, that the ethics of consent don’t apply for people who are starving.
Apple’s FaceID has a liveness detection feature, which prevents someone from unlocking a victim’s phone by putting it in front of his face while he’s sleeping. That feature has been hacked:
Researchers on Wednesday during Black Hat USA 2019 demonstrated an attack that allowed them to bypass a victim’s FaceID and log into their phone simply by putting a pair of modified glasses on their face. By merely placing tape carefully over the lenses of a pair glasses and placing them on the victim’s face the researchers demonstrated how they could bypass Apple’s FaceID in a specific scenario. The attack itself is difficult, given the bad actor would need to figure out how to put the glasses on an unconscious victim without waking them up.
MIT Technology Review is reporting about an infrared laser device that can identify people by their unique cardiac signature at a distance:
A new device, developed for the Pentagon after US Special Forces requested it, can identify people without seeing their face: instead it detects their unique cardiac signature with an infrared laser. While it works at 200 meters (219 yards), longer distances could be possible with a better laser. “I don’t want to say you could do it from space,” says Steward Remaly, of the Pentagon’s Combatting Terrorism Technical Support Office, “but longer ranges should be possible.”
Contact infrared sensors are often used to automatically record a patient’s pulse. They work by detecting the changes in reflection of infrared light caused by blood flow. By contrast, the new device, called Jetson, uses a technique known as laser vibrometry to detect the surface movement caused by the heartbeat. This works though typical clothing like a shirt and a jacket (though not thicker clothing such as a winter coat).
Remaly’s team then developed algorithms capable of extracting a cardiac signature from the laser signals. He claims that Jetson can achieve over 95% accuracy under good conditions, and this might be further improved. In practice, it’s likely that Jetson would be used alongside facial recognition or other identification methods.
Wenyao Xu of the State University of New York at Buffalo has also developed a remote cardiac sensor, although it works only up to 20 meters away and uses radar. He believes the cardiac approach is far more robust than facial recognition. “Compared with face, cardiac biometrics are more stable and can reach more than 98% accuracy,” he says.
I have my usual questions about false positives vs false negatives, how stable the biometric is over time, and whether it works better or worse against particular sub-populations. But interesting nonetheless.
This explainer highlights four broad trends in employee monitoring and surveillance technologies:
Prediction and flagging tools that aim to predict characteristics or behaviors of employees or that are designed to identify or deter perceived rule-breaking or fraud. Touted as useful management tools, they can augment biased and discriminatory practices in workplace evaluations and segment workforces into risk categories based on patterns of behavior.
Biometric and health data of workers collected through tools like wearables, fitness tracking apps, and biometric timekeeping systems as a part of employer- provided health care programs, workplace wellness, and digital tracking work shifts tools. Tracking non-work-related activities and information, such as health data, may challenge the boundaries of worker privacy, open avenues for discrimination, and raise questions about consent and workers’ ability to opt out of tracking.
Remote monitoring and time-tracking used to manage workers and measure performance remotely. Companies may use these tools to decentralize and lower costs by hiring independent contractors, while still being able to exert control over them like traditional employees with the aid of remote monitoring tools. More advanced time-tracking can generate itemized records of on-the-job activities, which can be used to facilitate wage theft or allow employers to trim what counts as paid work time.
Gamification and algorithmic management of work activities through continuous data collection. Technology can take on management functions, such as sending workers automated “nudges” or adjusting performance benchmarks based on a worker’s real-time progress, while gamification renders work activities into competitive, game-like dynamics driven by performance metrics. However, these practices can create punitive work environments that place pressures on workers to meet demanding and shifting efficiency benchmarks.
In a blog post about this report, Cory Doctorow mentioned “the adoption curve for oppressive technology, which goes, ‘refugee, immigrant, prisoner, mental patient, children, welfare recipient, blue collar worker, white collar worker.'” I don’t agree with the ordering, but the sentiment is correct. These technologies are generally used first against people with diminished rights: prisoners, children, the mentally ill, and soldiers.
One attraction of a vein based system over, say, a more traditional fingerprint system is that it may be typically harder for an attacker to learn how a user’s veins are positioned under their skin, rather than lifting a fingerprint from a held object or high quality photograph, for example.
But with that said, Krissler and Albrecht first took photos of their vein patterns. They used a converted SLR camera with the infrared filter removed; this allowed them to see the pattern of the veins under the skin.
“It’s enough to take photos from a distance of five meters, and it might work to go to a press conference and take photos of them,” Krissler explained. In all, the pair took over 2,500 pictures to over 30 days to perfect the process and find an image that worked.
They then used that image to make a wax model of their hands which included the vein detail.
Researchers areabletocreate fake fingerprints that result in a 20% false-positive rate.
The problem is that these sensors obtain only partial images of users’ fingerprints — at the points where they make contact with the scanner. The paper noted that since partial prints are not as distinctive as complete prints, the chances of one partial print getting matched with another is high.
The artificially generated prints, dubbed DeepMasterPrints by the researchers, capitalize on the aforementioned vulnerability to accurately imitate one in five fingerprints in a database. The database was originally supposed to have only an error rate of one in a thousand.
Another vulnerability exploited by the researchers was the high prevalence of some natural fingerprint features such as loops and whorls, compared to others. With this understanding, the team generated some prints that contain several of these common features. They found that these artificial prints were more likely to match with other prints than would be normally possible.
If this result is robust — and I assume it will be improved upon over the coming years — it will make the current generation of fingerprint readers obsolete as secure biometrics. It also opens a new chapter in the arms race between biometric authentication systems and fake biometrics that can fool them.
More interestingly, I wonder if similar techniques can be brought to bear against other biometrics are well.
Troy Hunt has a good essay about why passwords are here to stay, despite all their security problems:
This is why passwords aren’t going anywhere in the foreseeable future and why [insert thing here] isn’t going to kill them. No amount of focusing on how bad passwords are or how many accounts have been breached or what it costs when people can’t access their accounts is going to change that. Nor will the technical prowess of [insert thing here] change the discussion because it simply can’t compete with passwords on that one metric organisations are so focused on: usability. Sure, there’ll be edge cases and certainly there remain scenarios where higher-friction can be justified due to either the nature of the asset being protected or the demographic of the audience, but you’re not about to see your everyday e-commerce, social media or even banking sites changing en mass.
He rightly points out that biometric authentication systems — like Apple’s Face ID and fingerprint authentication — augment passwords rather than replace them. And I want to add that good two-factor systems, like Duo, also augment passwords rather than replace them.
On Friday, Vietnamese security firm Bkav released a blog post and video showing that — by all appearances — they’d cracked FaceID with a composite mask of 3-D-printed plastic, silicone, makeup, and simple paper cutouts, which in combination tricked an iPhone X into unlocking.
The article points out that the hack hasn’t been independently confirmed, but I have no doubt it’s true.
I don’t think this is cause for alarm, though. Authentication will always be a trade-off between security and convenience. FaceID is another biometric option, and a good one. I wouldn’t be less likely to use it because of this.
Three researchers from Michigan State University have developed a low-cost, open-source fingerprint reader which can detect fake prints. They call it RaspiReader, and they’ve built it using a Raspberry Pi 3 and two Camera Modules. Joshua and his colleagues have just uploaded all the info you need to build your own version — let’s go!
Sadly not the real output of the RaspiReader
We’ve probably all seen a movie in which a burglar crosses a room full of laser tripwires and then enters the safe full of loot by tricking the fingerprint-secured lock with a fake print. Turns out, the second part is not that unrealistic: you can fake fingerprints using a range of materials, such as glue or latex.
The RaspiReader team collected live and fake fingerprints to test the device
If the spoof print layer capping the spoofer’s finger is thin enough, it can even fool readers that detect blood flow, pulse, or temperature. This is becoming a significant security risk, not least for anyone who unlocks their smartphone using a fingerprint.
This is where Anil K. Jain comes in: Professor Jain leads a biometrics research group. Under his guidance, Joshua J. Engelsma and Kai Cao set out to develop a fingerprint reader with improved spoof-print detection. Ultimately, they aim to help the development of more secure commercial technologies. With their project, the team has also created an amazing resource for anyone who wants to build their own fingerprint reader.
So that replicating their device would be easy, they wanted to make it using inexpensive, readily available components, which is why they turned to Raspberry Pi technology.
The Raspireader and its output
Inside the RaspiReader’s 3D-printed housing, LEDs shine light through an acrylic prism, on top of which the user rests their finger. The prism refracts the light so that the two Camera Modules can take images from different angles. The Pi receives these images via a Multi Camera Adapter Module feeding into the CSI port. Collecting two images means the researchers’ spoof detection algorithm has more information to work with.
Real on the left, fake on the right
The Camera Adaptor uses the RPi.GPIO Python package. The RaspiReader performs image processing, and its spoof detection takes image colour and 3D friction ridge patterns into account. The detection algorithm extracts colour local binary patterns … please don’t ask me to explain! You can have a look at the researchers’ manuscript if you want to get stuck into the fine details of their project.
Build your own fingerprint reader
I’ve had my eyes glued to my inbox waiting for Josh to send me links to instructions and files for this build, and here they are (thanks, Josh)! Check out the video tutorial, which walks you through how to assemble the RaspiReader:
Building a cost-effective, open-source, and spoof-resilient fingerprint reader for $160* in under an hour. Code: https://github.com/engelsjo/RaspiReader Links to parts: 1. PRISM – https://www.amazon.com/gp/product/B00WL3OBK4/ref=oh_aui_detailpage_o05_s00?ie=UTF8&psc=1 (Better fit) https://www.thorlabs.com/thorproduct.cfm?partnumber=PS611 2. RaspiCams – https://www.amazon.com/gp/product/B012V1HEP4/ref=oh_aui_detailpage_o00_s00?ie=UTF8&psc=1 3. Camera Multiplexer https://www.amazon.com/gp/product/B012UQWOOQ/ref=oh_aui_detailpage_o04_s01?ie=UTF8&psc=1 4. Raspberry Pi Kit: https://www.amazon.com/CanaKit-Raspberry-Clear-Power-Supply/dp/B01C6EQNNK/ref=sr_1_6?ie=UTF8&qid=1507058509&sr=8-6&keywords=raspberry+pi+3b Whitepaper: https://arxiv.org/abs/1708.07887 * Prices can vary based on Amazon’s pricing. P.s.
You can find a parts list with links to suppliers in the video description — the whole build costs around $160. All the STL files for the housing and the Python scripts you need to run on the Pi are available on Josh’s GitHub.
Enhance your home security
The RaspiReader is a great resource for researchers, and it would also be a terrific project to build at home! Is there a more impressive way to protect a treasured possession, or secure access to your computer, than with a DIY fingerprint scanner?
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