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Scientists create AI neural net that can unlock digital fingerprint-secured devices
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.
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.
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.
Elon Musk
Tesla Semi’s official battery capacity leaked by California regulators
A California regulatory filing just confirmed the exact battery size inside each Tesla Semi variant.
A regulatory filing published by the California Air Resources Board in April 2026 has put official numbers on what Tesla Semi owners and fleet buyers have long wanted confirmed: the exact battery capacities of both the Long Range and Standard Range Semi truck variants. CARB is California’s independent air quality regulator, and it certifies zero-emission powertrains before they can be sold or operated in the state. When a manufacturer submits a vehicle for certification, the resulting executive order becomes a public document, making it one of the most reliable sources for confirmed production specs on any EV.
The document lists two certified powertrain configurations. The Long Range Semi carries a usable battery capacity of 822 kWh, while the Standard Range version comes in at 548 kWh. Both use lithium-ion NCMA chemistry and share the same peak and steady-state motor output ratings of 800 kW and 525 kW respectively. Cross-referencing Tesla’s published efficiency figure of approximately 1.7 kWh per mile under full load, the 822 kWh pack supports roughly 480 miles of real-world range, which aligns closely with Tesla’s advertised 500-mile figure for the Long Range trim. The 548 kWh Standard Range pack works out to approximately 320 miles, again consistent with Tesla’s stated 325-mile target.
Here is a direct comparison of the two versions based on the CARB filing and published specs:
| Tesla Semi Spec | Long Range | Standard Range |
| Battery Capacity | 822 kWh | 548 kWh |
| Battery Chemistry | NCMA Li-Ion | NCMA Li-Ion |
| Peak Motor Power | 800 kW | 525 kW |
| Estimated Range | ~500 miles | ~325 miles |
| Efficiency | ~1.7 kWh/mile | ~1.7 kWh/mile |
| Est. Price | ~$290,000 | ~$260,000 |
| GVW Rating | 82,000 lbs | 82,000 lbs |
The timing of this certification is not incidental. On April 29, 2026, Semi Programme Director Dan Priestley confirmed on X that high-volume production is now ramping at Tesla’s dedicated 1.7-million-square-foot facility in Sparks, Nevada. A key advantage of the Nevada location is vertical integration: the 4680 battery cells powering the Semi are manufactured in the same complex, eliminating the supply chain bottleneck that had delayed the program for years.
Tesla’s long-term goal is to reach a production capacity of 50,000 trucks annually at the Nevada factory, which would represent roughly 20 percent of the entire North American Class 8 market. With CARB certification now in hand and the production line running, the regulatory and manufacturing groundwork for that target is in place.
Tesla became the first company to pass the United States government’s new Advanced Driver Assistance Systems (ADAS) testing with the Model Y, completing each of the new tests with a passing performance.
In a landmark announcement on May 7, the National Highway Traffic Safety Administration (NHTSA) declared the 2026 Tesla Model Y the first vehicle to pass its newly ADAS benchmark under the New Car Assessment Program (NCAP).
Model Y vehicles manufactured on or after November 12, 2025, met rigorous pass/fail criteria for four newly added tests—pedestrian automatic emergency braking, lane keeping assistance, blind spot warning, and blind spot intervention—while also satisfying the program’s original four ADAS requirements: forward collision warning, crash imminent braking, dynamic brake support, and lane departure warning.
The NHTSA has just officially announced that the 2026 @Tesla Model Y is the first vehicle model to pass the agency’s new advanced driver assistance system tests.
2026 Tesla Model Y vehicles, manufactured on or after Nov. 12, 2025, successfully met the new criteria for four… pic.twitter.com/as8x1OsSL5
— Sawyer Merritt (@SawyerMerritt) May 7, 2026
NHTSA administration Jonathan Morrison hailed the achievement as a milestone:
“Today’s announcement marks a significant step forward in our efforts to provide consumers with the most comprehensive safety ratings ever. By successfully passing these new tests, the 2026 Tesla Model Y demonstrates the lifesaving potential of driver assistance technologies and sets a high bar for the industry. We hope to see many more manufacturers develop vehicles that can meet these requirements.”
The updates to NCAP, finalized in late 2024 and effective for 2026 models, reflect growing recognition that ADAS features are no longer optional luxuries but essential tools for preventing crashes.
Pedestrian automatic emergency braking, for instance, targets one of the fastest-rising causes of roadway fatalities, while blind spot intervention and lane keeping assistance address common sources of side-swipes and run-off-road incidents. By incorporating objective, performance-based evaluations rather than mere presence of the technology, NHTSA aims to give buyers clearer data on real-world effectiveness.
This milestone arrives at a pivotal moment when vehicle autonomy is transitioning from science fiction to everyday reality.
Tesla’s Full Self-Driving (FSD) software and the impending rollout of robotaxis underscore a broader industry shift toward higher levels of automation. Yet regulators and consumers remain cautious: safety data must keep pace with technological ambition.
The Model Y’s perfect score on these ADAS benchmarks validates that current driver-assist systems—when engineered rigorously—can dramatically reduce human error, which still accounts for the vast majority of crashes.
For Tesla, the result reinforces its long-standing claim of building the safest vehicles on the road. More importantly, it signals to the entire auto sector that meeting elevated federal standards is achievable and expected.
As autonomy edges closer to Level 3 and beyond, where drivers may disengage more fully, such independent verification becomes critical. It builds public trust, informs purchasing decisions, and accelerates the development of systems that could one day eliminate tens of thousands of annual traffic deaths.
In an era when software-defined vehicles promise transformative mobility, the 2026 Model Y’s NHTSA triumph is more than a manufacturer accolade—it is a regulatory green light that autonomy’s future must be built on proven, testable safety foundations. The bar has been raised. The industry, and the roads we share, will be safer for it.
Tesla is going to fix the nearly 219,000 vehicles that it recalled due to an issue with the rearview camera with a simple software update, giving owners no need to travel to a service center to resolve the problem.
Tesla is formally recalling 218,868 U.S. vehicles after regulators discovered a software glitch that can delay the rearview camera image by up to 11 seconds when drivers shift into reverse.
The affected models include certain 2024-2025 Model 3 and Model Y, as well as 2023-2025 Model S and Model X vehicles running software version 2026.8.6 and equipped with Hardware 3 computers. The National Highway Traffic Safety Administration (NHTSA) determined the lag violates Federal Motor Vehicle Safety Standard 111 on rear visibility and could increase crash risk.
Yet this is no ordinary recall. Owners do not need to schedule a service-center visit, hand over keys, or wait for parts.
Tesla fans call for recall terminology update, but the NHTSA isn’t convinced it’s needed
Tesla identified the issue on April 10, halted further deployment of the faulty firmware the same day, and began pushing a corrective over-the-air (OTA) software update on April 11.
By the time the NHTSA posted the recall notice on May 6, more than 99.92 percent of the affected fleet had already received the fix. Tesla reports no crashes, injuries, or fatalities linked to the glitch.
The episode underscores a deeper problem with regulatory language. For decades, “recall” meant hauling a vehicle to a dealership for hardware repairs or replacements. That definition no longer fits software-defined cars. When a fix arrives wirelessly in minutes — identical to an iPhone update — the term evokes unnecessary alarm and misleads the public about the actual risk and remedy.
Elon Musk has repeatedly called for exactly this change. After earlier NHTSA actions, he stated plainly: “The terminology is outdated & inaccurate. This is a tiny over-the-air software update.” On another occasion, he added that labeling OTA fixes as recalls is “anachronistic and just flat wrong.”
The terminology is outdated & inaccurate. This is a tiny over-the-air software update. To the best of our knowledge, there have been no injuries.
— Elon Musk (@elonmusk) September 22, 2022
Musk’s point is simple: regulators must evolve their vocabulary to match the technology. Traditional recalls involve physical intervention and downtime; OTA updates do not. Retaining the old label distorts consumer perception, inflates perceived defect rates, and slows the industry’s shift to faster, safer software iteration.
Tesla’s rapid, remote remedy demonstrates the safety advantage of over-the-air capability. Problems that once required weeks of dealer appointments are now resolved in hours, often before most owners notice. As more automakers adopt software-first designs, the entire regulatory framework needs to catch up.
Updating “recall” terminology would align language with reality, reduce public confusion, and recognize that modern vehicles are no longer static hardware — they are continuously improving computers on wheels.
For the 219,000 Tesla owners involved, the process is already complete. The camera works, the car is safe, and no one left their driveway. That is the new standard — and the vocabulary should reflect it.
Tesla Semi’s official battery capacity leaked by California regulators
Tesla crushes NHTSA’s brand-new ADAS safety tests – first vehicle to ever pass
