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Scientists create AI neural net that can unlock digital fingerprint-secured devices

Fingerprint scan. | Credit: RCPA

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Computer scientists at New York University and Michigan State University have trained an artificial neural network to create fake digital fingerprints that can bypass locks on cell phones. The fakes are called “DeepMasterPrints”, and they present a significant security flaw for any device relying on this type of biometric data authentication. After exploiting the weaknesses inherent in the ergonomic needs of cellular devices, DeepMasterPrints were able to imitate over 70% of the fingerprints in a testing database.

An artificial neural network is a type of artificial intelligence comprising computer algorithms modeled after the human brain’s ability to recognize patterns. The DeepMasterPrints system was trained to analyze sets of fingerprint images and generate a new image based on the features that occurred most frequently. This “skeleton key” could then be used to exploit the way cell phones authenticate user fingerprints.

In cell phones, the necessarily small size of fingerprint readers creates a weakness in the way they verify a print. In general, phone sensors only capture a partial image of a print when a user is attempting to unlock the device, and that piece is then compared to the phone’s authorized print image database. Since a partial print means there are fewer characteristics to distinguish it than a full print, a DeepMasterPrint needs to match fewer features to imitate a fingerprint. It’s worth noting that the concept of exploiting this flaw is not unique to this particular study; however, generating unique images rather than using actual or synthesized images is a new development.

An overview of the DeepMasterPrint system. | Credit: IEEE

The team involved in the study resulting in the DeepMasterPrint creation initiated it as part of the ongoing assessment of vulnerabilities in fingerprint recognition systems. Finding exploitable flaws and fixing them is a constant battle in all digital systems with a security component. With this reality in mind, the scientists determined that merely exposing the flaws of fingerprint systems would not provide an effective solution; a working example of how attacks could be executed provides more specific data for researchers to design around and protect against. Creating the DeepMasterPrint system was meant to address this need.

The results revealed by the DeepMasterPrint system are concerning for anyone relying on fingerprint authentication on their smartphones. Scientists compared the generated fake prints against templates generated by VeriFinger 9.0 SDK, Bozorth3, and Innovatrics IDKit 5.3 SKD, all of which are software systems used in fingerprint authentication systems worldwide. At a low false match rate, i.e., strict match requirements for authentication, the fake print generated by DeepMasterPrint could imitate 23% of the fingerprints in the test database. At a slightly higher false match rate that was still within standard phone authentication limits, the fake print imitated 77% of the test fingerprints.

Real fingerprints on the left vs. DeepMasterPrint generated fingerprints on the right. | Credit: IEEE

The scientists in this study did not create physical fingerprints to try and unlock actual phones, leaving that work to be done in the near future. However, even though the successful DeepMasterPrints have yet to be tested in true applications rather than a virtual environment, the data gathered confirmed the initial security concerns which inspired the experiment. Fingerprints are being used as identity verification in a growing number of applications beyond cell phone security, i.e., unlocking entryways, payment authentication, etc. The DeepMasterPrint system is another tool to help researchers guard their security as biometric authentication continues to expand.

Accidental computer geek, fascinated by most history and the multiplanetary future on its way. Quite keen on the democratization of space. | It's pronounced day-sha, but I answer to almost any variation thereof.

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Starlink launches Communities Program for passive income through internet sharing

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(Credit: Starlink | X)

Starlink is launching a new beta path for ordinary property owners and local operators to turn a single Starlink kit into a small shared-access business for passive income.

Under the Starlink for Communities program, a host installs one dish and router setup in a location with nearby demand: an apartment complex, campground, rural crossroads, or event site. Neighbors or local users can buy short-term passes rather than full individual subscriptions, giving the Starlink provider a potential path to passive income.

Hour, day, and week passes cover one device. A month pass covers up to four. Starlink handles account creation, payments, access controls, and the satellite link itself. The host’s role is mainly placement, power, and basic upkeep, with earnings tied to each paid connection.

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The model echoes the passive-income vision long attached to Tesla’s Robotaxi plans, and it seems like it’s something Musk has hinted toward in the past as he believes AI will make the need to work relatively optional. In both cases, the platform owns the hard parts of matching, billing, and network management, while an individual supplies a physical asset that sits idle much of the time.

A Starlink host’s dish can serve multiple nearby users without each household buying and installing its own terminal. A Tesla owner, under the stated Robotaxi concept, would leave a vehicle enrolled in the fleet during unused hours so the car generates rides while the owner is at work or asleep.

Both arrangements convert under-utilized hardware into a revenue stream. They also let the company scale coverage or capacity without owning every endpoint.

Differences are practical. A Starlink kit is a fixed, relatively low-cost terminal whose main constraint is local congestion and line-of-sight. A Tesla Robotaxi is a mobile, high-value vehicle whose earnings depend on demand density, utilization rates, insurance, cleaning, and charging.

Starlink’s program is already accepting host applications in multiple countries and describes the revenue split as ongoing. Tesla’s owner-network version remains more aspirational.

The company currently operates a limited company-controlled robotaxi service in select areas and has solicited interest from fleet buyers for Cybercab vehicles, while private Full Self-Driving owners have not yet been able to dispatch their own cars for paid rides at scale.

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Tesla primes Cybercabs for 4K streaming and high bandwidth gaming with Starlink integration

Starlink is a satellite broadband service operated by SpaceX that uses a constellation of low-Earth-orbit satellites to deliver internet to locations where terrestrial broadband is slow, expensive, or absent. It has grown to millions of subscribers worldwide by selling direct residential, mobile, and enterprise terminals, and have become widely available at a wide array at retail locations like Target and Best Buy.

The Communities program extends that reach by letting hosts resell short bursts of capacity to people nearby, while also providing high-speed internet access to those who are simply around a Starlink user.

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Tesla just made its headlights even better through a software update

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Credit: @jojje167 on X

Tesla just upgraded its headlights through a software update, making them even better without any physical or hardware upgrade.

Tesla’s latest software update is quietly improving nighttime driving for a small number of owners. Version 2026.38 includes a new capability called Dynamic Headlight Leveling.

The feature automatically adjusts the aim of the low beams in response to driving conditions and nearby traffic, with the goal of giving the driver more usable light on the road while reducing glare for oncoming vehicles and traffic ahead.

Unlike Tesla’s matrix high-beam system, which selectively dims individual LED segments to create shadows around other cars, Dynamic Headlight Leveling physically tilts the low-beam projectors. Internal motors respond to changes in vehicle pitch.

When the car accelerates hard, climbs a steep grade, or carries extra weight in the rear, the headlights can otherwise point higher than intended. The software counters that movement in real time so the beam stays aimed at the road surface rather than into the eyes of other drivers.

Early indications reveal the update is reaching a limited set of vehicles, including certain Model 3 and Cybertruck examples in the United States and the United Arab Emirates. The rollout does not appear tied to a single hardware revision, and Tesla has not published a broader schedule. It is simply a common waiting game until your car receives it.

The change arrives against a backdrop of wider complaints about headlight glare. Some earlier Model 3 and Model Y vehicles were the subject of an NHTSA recall related to excessive low-beam glare; the software adjustment offers a potential mitigation for cars equipped with the necessary leveling hardware. It does not replace adaptive high beams where those are already available, nor does it alter the basic low-beam pattern itself.

Instead, it keeps an existing beam pointed where it is most useful.

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For drivers who have received the update, the system requires no new settings or user input. The headlights simply respond as road conditions and traffic change. As the feature reaches more vehicles, it adds another example of Tesla using over-the-air software to refine existing hardware rather than waiting for a new model year. Nighttime visibility and reduced glare for others are the practical results owners are expected to notice first.

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Tesla teases “Halloween Mode” update with Optimus rising from a graveyard

Tesla’s Halloween teaser hides a covered vehicle and an Optimus hand rising from the ground.

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Tesla has started teasing a Halloween software update for its vehicles, with  a short clip on X that reads, “Halloween is coming.” The clip opens on a glowing pumpkin before pulling back to the car’s center touchscreen, where the usual parked visualization has been replaced by a graveyard scene, and the vehicle draped with a white sheet so it reads as a cartoon ghost.

The second detail is a robotic hand clawing its way out of the dirt like a zombie, which looks to be the hand of Tesla’s latest Optimus V3 humanoid robot. While Tesla still has not formally shown Optimus Gen 3 walking around in service, renders pulled from Tesla’s Android app last month gave the clearest look yet, including far more refined hands that Tesla has said carry 22 degrees of freedom. The hand has been the hardest part of the program. Musk has called it the majority of the robot’s engineering difficulty, and Tesla’s patents describe a design driven by tendons with the actuators moved into the forearm.

Tesla Optimus V3 hand and arm details revealed in new patents

Optimus also has a Halloween track record. Last October the robot handed out candy in Times Square, and a costumed “zombie” Optimus shuffled around the Tesla Diner in Los Angeles on Halloween night.

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On the software side, Tesla’s 2025 Holiday Update expanded Santa Mode with a Santa sleigh, snowmen, snow effects, and a festive lock chime, so it wouldn’t be too far fetched if we saw something similar but themed for a  Halloween Mode.

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