This is probably worth paying attention to:
Posted on June 14, 2021 at 10:11 AM •
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 AM •
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.
EDITED TO ADD: This post has been translated into Spanish.
Posted on August 21, 2020 at 6:03 AM •
Coming out of the Privacy Commissioners’ Conference in Albania, Public Voice is launching a petition for an international moratorium on using facial recognition software for mass surveillance.
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.
Posted on October 22, 2019 at 10:12 AM •
This article discusses new types of biometrics under development, including gait, scent, heartbeat, microbiome, and butt shape (no, really).
Posted on September 20, 2019 at 6:12 AM •
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.
Posted on August 20, 2019 at 6:45 AM •
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.
Posted on August 15, 2019 at 6:19 AM •
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.
Posted on July 8, 2019 at 12:38 PM •
Nice bit of adversarial machine learning. The image from this news article is most of what you need to know, but here’s the research paper.
Posted on April 25, 2019 at 6:31 AM •
Data & Society just published a report entitled “Workplace Monitoring & Surveillance“:
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.
Posted on March 12, 2019 at 6:38 AM •
Sidebar photo of Bruce Schneier by Joe MacInnis.