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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.

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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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Tesla Full Self-Driving v14.3.5 Early Impressions: new features and early performance

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Credit: TESLARATI

Tesla rolled out Full Self-Driving (Supervised) v14.3.5 yesterday, and about fifty miles of driving on the new version has given me enough time to highlight what seems to be strong about the release and what is not.

Additionally, Tesla has added a few new features with this specific update, which we’ll highlight as well.

Tesla Full Self-Driving v14.3.5 Performance

The new update is business as usual. Things seem to be running completely normal and necessary, but there are a few things that we’ve seemed to pick up on based on our own experience with v14.3.5, as well as what other users are seeing.

Initially, it seems to be more aware of its surroundings, making moves that are incredibly courteous to other drives and operating just a tad more reserved than what the suite might have done previously.

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We had two instances where it showed this, the first being FSD needing to pass a Flagger Force vehicle that was placing down signage for the day. Their work truck was right at the front corner of a right-hand turn; typically where most cars travel when they take that turn.

FSD v14.3.5 recognized this, slowed down, and took the turn wide with no issues:

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Additionally, v14.3.5 backed up for a semi truck that was making a wide turn onto a road my car was on. This is not new, but it seemed to be backing up for courtesy; it didn’t seem completely necessary, but it might have put some peace of mind in the truck driver’s head:

X user Mike P, also a Pennsylvania native like myself, shared three clips of his Tesla running v14.3.5 performing similar maneuvers. He said:

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“FSD turns right into a small alley that only fits one car at a time, sees oncoming car, reverses out of alley to make space, realizes oncoming car is actually parking, re-enters alley.”

Check it out here:

It seems like Speed Profiles are still in need of some tweaking; I am adjusting what Speed Profile I’m in frequently, constantly changing it to get it to travel at the correct speed. This was an issue for me on v14.3.4. It seems like they’re just a little inconsistent.

Terrible Parking

Parking attempts on v14.3.5 were not good. There are quite a few people who have said this:

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David Moss, the Tesla owner who has taken multiple coast-to-coast drives without any interventions, also has had some issues with parking early on with v14.3.5:

New Features

Tesla has added the ability to open Camera Preview at any time. Previously, it was only available in Park. Here’s what that feature looks like in action:

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Check back later this week for a longer review of what we’ve noticed on Full Self-Driving v14.3.5.

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Tesla makes the cut on California’s newest EV Rebate program

California just signed a $270 million EV rebate into law and it starts this summer.

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California Governor Gavin Newsom signed SB 168 into law on Monday, July 13, 2026, creating a $270 million EV rebate program that delivers money directly at the dealership rather than as a tax credit applied months later. The program, called MyFirstEV, is funded equally by California’s state budget and participating automakers, with each contributing $135.5 million to make the math work.

The timing is directly tied to the loss of federal support when the $7,500 federal EV tax credit ended, removing the most significant consumer incentive that had driven EV adoption in the U.S. California, which accounts for roughly one-third of all EVs sold nationally, moved to fill that gap with a state-level replacement.

The rebate structure is straightforward. First-time EV buyers can receive $3,500 off any new battery-electric vehicle with an MSRP up to $50,000. Used EVs priced at $25,000 or below qualify for a $1,750 rebate. The credit is applied at the point of sale, which removes the friction of the old federal system where buyers had to wait for tax season to see the benefit. The program goes live later this summer, with the California Air Resources Board expected to release full participation details next month.

California hits Tesla Cybercab and Robotaxi driverless cars with new law

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For Tesla buyers, the implications are mixed. The Tesla Model 3 RWD at $42,490 and the Model 3 Long Range at $47,490 both fall under the $50,000 cap and would qualify for the full $3,500 rebate for first-time buyers. The Model Y, which starts at $44,990 after Tesla’s recent price adjustment, also qualifies. The Model X, Model S, and Cybertruck all exceed the cap and receive no benefit. As Teslarati has reported, the program also includes a carve-out exempting California-based automakers like Rivian and Lucid from the price cap entirely, a provision that puts Tesla at a disadvantage since it relocated its headquarters to Texas in 2021.

Other qualifying vehicles include the Chevrolet Equinox EV, Ford Mustang Mach-E, Hyundai Ioniq 5, Kia EV6, and Volkswagen ID.4.

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Tesla Semi enters new Pilot Program with interesting challenge

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Credit: PTI

The Tesla Semi is entering a new Pilot Program with Paper Transport, LLC (PTI), a Wisconsin-based transportation provider. The company will test the Semi’s Long Range configuration through “dedicated operations within the Chicago market.”

Chicago presents an interesting challenge for the Semi, as it will be a colder-weather climate that will test the Semi’s ability to operate in lower temperatures and in potentially large accumulations of snow. This is something Tesla has been testing with the Semi in Alaska and even in Northern California during the colder months, but Chicago will present a truly tough midwestern winter.

Tesla Semi spotted on journey home after winter performance testing

PTI says it is using the Semi to evaluate its strategy of reducing transportation emissions while maintaining performance, reliability, and cost efficiency. These are major arguments for the Semi being introduced into new fleets.

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CEO of PTI Tyler Ellison said:

“PTI has been a leader in sustainable transportation solutions for over 15 years. We take a consultative approach to helping customers identify and implement the right transportation solution for their network. Our partnership with Tesla expands our portfolio alongside renewable natural gas and intermodal, giving customers more ways to reduce Scope 3 emissions without compromising service or economics.”

PTI is far from the first company to adopt the Semi within a fleet, as Tesla entered strategic agreements with PepsiCo. and its subsidiary Frito-Lay for a Pilot Program that extended throughout the California region.

Tesla has let companies like those utilize the Semi to determine whether it would be suitable for their operations. Additionally, Tesla gets valuable information regarding the Semi’s performance, knowing what to improve and what is ideal for companies that will utilize the all-electric truck for regional and nationwide logistics.

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PTI plans to utilize the Long Range configuration, which is priced at $290,000 and features a range of approximately 500 miles, a three-motor powertrain, up to 800 kW of drive power, and consumption of just 1.7 kWh per mile.

Tesla Semi pricing revealed after company uncovers trim levels

VP of Maintenance at PTI, Bryan Ellen, added:

“We are excited to partner with Tesla, leveraging their ever-evolving technology. We are bullish in our estimation of the parallels available between our dedicated model and the efficiency of their fully electric Class 8 tractor. We anticipate a growing synergy between our businesses as we work to facilitate this sustainable solution for our customers.”

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PTI has logged more than 87 million miles using sources like compressed and renewable gas, but now is looking to take it a step further with fully electric operations.

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