Over the weekend, two popular gay bars in San Francisco halted the use of ID verification technology after pushback. Badlands and Toad Hall, both in the Castro neighborhood, opted to pause using Patronscan Guard+, an AI assisted identification reader, similar to what you may see at an airport, after community concerns were raised. Watch NBC Bay Area News free wherever you are Patronscan is an ID scanning technology that can identify fake IDs at a forensic level through comparisons with known fake templates and comparisons with government provided security measures. Servall Data Systems, the makers of Patronscan, also offers a flagging service that can help bars and venues alert their employees of problematic customers and share that information with other establishments that utilize this technology. Opponents are concerned that it is data collection of vulnerable populations and yet another form of tech surveillance in the age of license plate readers and even facial scanning AI. Fight for the Future, an advocacy organization that has been working to prevent AI surveillance technology from being utilized in music venues and bars around the country, called for a boycott all bars in San Francisco that use Patronscan, until they discontinue its use. "We have heard the concerns regarding the use of Patronscan at Badlands and Toad Hall in San Francisco, and we are listening," said Brian Aranda, director of operations at both bars, in a statement released on Friday. "Effective immediately, we are pausing the use of Patronscan while we review our ID verification and security practices. The safety of our guests and staff remains a priority, as do privacy and trust. Our security teams will continue to thoroughly check IDs manually during this time." Evan Greer, director of Fight for the Future said that their organization has been working on campaigns to
Aug 11, 2026 · via nbcbayarea.com
Figures Abstract As a fundamental acoustic component, the buzzer is widely used in various electronic systems. The iron core is a critical element in buzzers for supporting the coil, and it is currently fed primarily by mechanical methods. To further improve the automatic feeding efficiency of iron cores, a machine vision-based detection method is proposed to achieve core localization, pose recognition, and notch-angle measurement. The method first employs the Hough transform to locate iron cores on the vibratory tray and exclude overlapping cores. It then statistically counts the edge pixels around each core's center to select only those facing upward. Finally, by traversing the core's circumference, it pinpoints the notch localization and computes its angle. This providing the necessary data support for the automatic grasping and placement of iron cores by the manipulator. Experimental results demonstrate that the Hough transform algorithm adopted in this paper achieves a mean relative localization error of only 2.61%, a recognition precision of 100% for front-up iron cores, and an average notch-angle measurement deviation of 1.06°. Compared with YOLOv8, the proposed method offers clear advantages in both detection accuracy and practicality. Citation: Liu X, Sun C, Wang C, Huang X, Zhu R, Hu C (2026) Machine vision-based detection method for buzzer iron cores. PLoS One 21(8): e0354351. https://doi.org/10.1371/journal.pone.0354351 Editor: Wislei Riuper Osório, UNICAMP, University of Campinas, BRAZIL Received: April 25, 2026; Accepted: July 7, 2026; Published: August 11, 2026 Copyright: © 2026 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The data underlying the results of this study are available from GitHub (https://github.com/obito0330/Leoxy). Funding: This research was funded by National Nature Science Foundation of
Aug 11, 2026 · via journals.plos.org
Moscow police beat 15-year-old after AI facial recognition system misidentifies him as drug suspect Moscow police beat a schoolboy after an AI-powered facial recognition system mistakenly flagged him as a criminal suspect, journalist Ksenia Sobchak reported. The incident took place in early May in Moscow’s Lomonosovsky District, the boy’s father said. Two police officers attacked the 15-year-old from behind, knocked him to the ground, and beat him — then took him to a police station and accused him of distributing drugs. Police did not call the boy’s mother until two hours later, telling her that her son was a suspect based on data from an AI system that had allegedly found an 85–90% match with a person named in a wanted notice. After questioning, the mother and son were released. Doctors later diagnosed the teenager with a concussion, a head injury, hematomas, bruises, and abrasions. According to the official police account, the teenager actively resisted arrest, and officers used “combat fighting techniques” against him. The boy’s parents have appealed to the Investigative Committee, Russia’s principal federal investigative agency, but for four months have received only form letters saying a review is underway. At Meduza, we are committed to transparency about our use of artificial intelligence in the newsroom. The story you’re reading was written by one of our living, breathing journalists and translated from Russian using an AI model configured to follow our strict editorial standards. This translation process is the result of extensive testing and refinements to ensure our English-language coverage is timely and accurate. A Meduza editor reviews every draft before publication. If you find any errors in this translation, please contact us at [email protected]. To read Meduza’s exclusive content in English, please subscribe to our newsletter.
Aug 11, 2026 · via meduza.io
Figures Abstract Salient Object Detection (SOD) is concerned with isolating the visually most noticeable objects in an image via precise segmentation. Previous approaches, especially those that adapt the Segment Anything Model (SAM), often yield saliency maps that contain incomplete object masks, blurred boundaries, and a lack of fine-grained details. This issue is especially severe for scenes with objects of different scales or complex textures. We argue that these issues stem from three inherent limitations of existing adaptation strategies: (1) existing adapters rely on rigid multi-scale fusion strategies, lacking learnable cross-scale calibration to handle objects of diverse sizes, (2) simple feature concatenation ignores cross-level semantic correlations, (3) spatial-domain operations inevitably discard high-frequency details. The proposed WMW-SAM, a Weighted Multi-Scale and Wavelet-Enhanced SAM, is designed to handle these limitations for SOD. Specifically, we develop the Weighted Multi-Scale Adapter (WMSA), which utilizes learnable weighting across multiple receptive fields to calibrate features of different scales. Then, our Multi-level Feature Cross-fusion Module (MFCM) employs cascaded top-down cross-attention to facilitate deep interaction that connects high-level semantics with low-level details. Finally, we develop a Detail Enhancement Module (DEM) that leverages the Discrete Wavelet Transform (DWT) to explicitly extract and enhance high-frequency sub-bands. This operation effectively recovers sharp boundaries and intricate textures, which are often overlooked by spatial-domain operations. Extensive experiments on multiple benchmark datasets demonstrate the superior performance of our proposed WMW-SAM, which achieves accurate and detailed saliency predictions. Citation: Liu Z, Tian D (2026) Weighted multi-scale and wavelet-enhanced Segment Anything Model for salient object detection. PLoS One 21(8): e0355742. https://doi.org/10.1371/journal.pone.0355742 Editor: Yongjie Li, University of Electronic Science and Technology of China, CHINA Received: May 4, 2026; Accepted: July 25, 2026; Published: August 11, 2026 Copyright: © 2026 Liu, Tian. This is an open access article distributed under the terms of the Creative Commons Attribution License, which
Aug 11, 2026 · via journals.plos.org
Patronscan ID verification paused at popular gay bars in San Francisco due to privacy concerns San Francisco’s Castro District is rolling back the use of AI‑assisted face scanning technology after LGBTQ+ community members raised alarms over privacy, surveillance and the creation of sensitive data profiles. Two prominent gay bars, Badlands and Toad Hall, said they would stop using Patronscan ID verification following weeks of criticism. “We have heard about the concerns regarding the use of PatronScan at Badlands and Toad Hall in San Francisco, and we are listening,” Toad Hall said on an Instagram post. “Effective immediately, we are pausing the use of Patronscan while we review our ID verification and security practices.” Staff will return to manual ID checks while the review is underway. The scanners were introduced earlier this year but have faced backlash. Patrons say the technology felt intrusive in spaces historically seen as refuges for people who may not be publicly out. The venues use an ID scanner and a camera — Patronscan Guard+ — that captures photos of patrons. These are stored on a database, for up to 21 days, and are shared on a network to which venues can access if they subscribe. Eight venues in San Francisco use the technology, according to SFGate. Patronscan’s system collects a customer’s ZIP code, photograph, date of birth, gender and the expiration date of their ID, according to the company’s website. Company spokesperson Rhiannon Mosoronchon told SFGate that it does not use facial recognition but is stored as a timestamped image of the patron. The company says data is stored for about 21 days to assist law enforcement investigations and is not sold or shared with third parties. It argues its technology complies with California law, which permits ID verification systems to prevent underage drinking and fraud.
Aug 11, 2026 · via biometricupdate.com
Vadzo Imaging validates Real Time Dynamic ROI streaming on Bolt-2020CRS Full 20MP Color MIPI camera for Embedded Vision Applications Vadzo Imaging's Bolt-2020CRS pairs 20MP color resolution with the Onsemi AR2020 sensor's enhanced near infrared response giving security, robotics, and industrial camera products usable color detail from bright daylight through dusk, and also supports Dynamic ROI functionality, allowing embedded vision systems to selectively capture and process important areas within the frame while reducing processing load and optimizing bandwidth utilization. FORT WORTH, Texas, August 11, 2026 (Newswire.com) - Vadzo Imaging, a provider of embedded vision camera products for OEMs and system integrators, today announced the Bolt-2020CRS 20MP Color MIPI Camera built on the Onsemi AR2020 sensor. The new camera module extends color imaging performance into low light and near infrared conditions that typically force a switch to a monochrome sensor or additional visible light illumination. Security integrators, robotics developers, and industrial inspection teams gain a single MIPI CSI-2 camera that holds resolution, color accuracy, and exposure stability from daylight through near dark scenes. The Low Light Color Imaging Problem in Embedded MIPI Camera Design Standard color image sensors lose a large share of incoming photons to the Bayer filter array that separates red, green, and blue light before it reaches each pixel. That loss becomes severe as ambient light drops toward dusk, indoor low light, or nighttime conditions common in security corridors, warehouse aisles, and outdoor inspection stations. Many embedded vision programs respond by switching to a monochrome sensor after dark, which sacrifices color information exactly when identification tasks such as license plate reading, apparel color matching, or intruder clothing description need it most. Adding supplemental visible light illumination is often not possible in covert security placements, wildlife-adjacent industrial sites, or battery-powered robotics platforms where every added watt shortens runtime. Dynamic ROI
Aug 11, 2026 · via newswire.com
Mayflower has become the second Central Arkansas city in the last week to ditch its Flock Safety license plate readers. Interim Police Chief Brittany Byrd announced Monday that after “evaluating the capabilities” of the cameras, Mayflower will discontinue their use and will “rescind their deployment within the city immediately.” The move comes three days after the city of Cabot announced its decision to stop using Flock cameras pending a “comprehensive review.” Last month, the police chief in Pea Ridge announced his city would no longer use the devices. In her announcement, Byrd acknowledged Flock cameras have aided in the investigation of criminal cases across the country, including in Mayflower. But despite the benefits, she wrote, “we believe them to be intrusive in nature.” “The Mayflower Police Department will not participate in tactics which compromise or infringe upon the liberties entitled to law-abiding citizens,” Byrd wrote. Flock license plate reader cameras take pictures of passing cars and log their license plate numbers, comparing the captured information against so-called hot lists assembled by the law enforcement agencies that operate them. Flock Safety, the company that manufactures the devices, states on its website that the cameras don’t use facial recognition software but boast the ability to search captured images based on a description of a vehicle — such as its color, make and model, or unique features such as bumper stickers — if a plate number isn’t known. Many cities in Arkansas use the cameras, including Little Rock, North Little Rock, Conway, Hot Springs, Benton, Bryant, Jacksonville and Sherwood. Nationally, more than 5,000 law enforcement agencies have bought Flock’s license plate readers, the company’s website states. Byrd said Mayflower signed a multi-year agreement with Flock Safety in 2023 that runs through 2027. “Police Department personnel have been communicating with representatives from Flock Safety
Aug 11, 2026 · via nwaonline.com
Courtesy of Gentle MonsterFashion / LongreadHow AI infiltrated fashion, one It Girl at a timeBy making its technology chic and aspirational, the AI industry attempts to win over its sceptics – but is everyone falling for the ruse?ShareLink copied ✔️August 11, 2026FashionLongreadAugust 11, 2026Text Natasha Cornelissen AI no longer wants to limit itself to a techy male consumer base. Increasingly, it’s striving to become a cool-girl staple, with companies seeking to capitalise on the trend-setting potential of celebrities, influencers and the cloutier corners of online culture. From Steven Meisel-shot smart glasses starring Kaia Gerber and Hoyeon Jung to Gentle Monster’s forthcoming AI eyewear fronted by Anok Yai and Alex Consani, fashion and AI are becoming ever more intertwined. The timing is significant. According to recent research, women are more sceptical of AI than men and more likely to think it will have a negative impact on society. For those betting big on AI’s rise, this poses a significant challenge. Despite the gender pay gay gap, women are responsible for up to 85 per cent of household purchasing decisions in the US, possessing a spending power of over $17 trillion. In line with their increasing financial independence and higher levels of disposable income, women are a more important market for luxury brands than ever before. Consider the latest product release from Snapchat’s parent company Snap: augmented reality (AR) glasses, called SPECS. Designed with a sleek aviator frame and made from what Snap calls “plastic titanium”, the $2,195 glasses are positioned as much as a fashion object as a piece of technology. They were launched in June with a campaign shot by legendary fashion photographer Steven Meisel. Cult eyewear designer Gentle Monster has also hopped on the bandwagon, partnering with Google and Samsung on its own AI glasses, with more designs due
Aug 11, 2026 · via dazeddigital.com
Live facial recognition technology is set to be used at selected London Underground stations as part of a new British Transport Police (BTP) trial aimed at identifying people wanted for serious offences. The first deployment will take place at Victoria station on Tuesday, with the technology expected to be used at other key Tube stations during the trial. Transport for London (TfL) said the cameras will compare images of people passing through designated areas with a police watchlist. The system is intended to help officers identify people wanted in connection with high-harm offences, including sexual offences, robbery and knife crime. BTP will decide where and when to deploy the technology using intelligence and analysis of crime data, with the aim of targeting locations where it could have the greatest impact. TfL said that images of people who do not match anyone on the police watchlist will be automatically deleted immediately. The trial will operate within clearly marked areas at stations. TfL said this will allow passengers to make an informed decision about whether to pass through the areas, while BTP said alternative routes will be available. The Tube operation expands on a separate BTP trial involving major railway stations across London that began in February. Between February and July, facial recognition technology was deployed 18 times at major London railway stations, during which more than 530,000 faces were scanned. According to BTP’s published deployment records, none of those scans resulted in a confirmed match or an arrest. The system generated one false identification during the period. Siwan Hayward, director of security, policing and enforcement at TfL, said: “Everyone should be able to travel without fear of harassment, intimidation or violence. “Tackling violence against women and girls and preventing sexual offending on our network remains a key priority. “Live facial recognition
Aug 11, 2026 · via londragazete.com
Abstract Foundation models (FMs) are driving a prominent shift in biomedical imaging, from task-specific models to unified backbone models for diverse tasks. This opens an avenue to integrate imaging, pathology, clinical records and genomics data into a composite system. However, this vision contrasts sharply with modern medicine’s trajectory towards more granular sub-specialization. This tension, coupled with data scarcity, domain heterogeneity and limited interpretability, creates a gap between benchmark success and real-world clinical value. We argue that the immediate role of FMs lies in augmenting, not replacing, clinical expertise. To separate hype from reality, we introduce real-world evaluation and assessment of FMs (REAL-FM), a multi-dimensional framework assessing data, technical readiness, clinical value, workflow integration and responsible artificial intelligence. Using REAL-FM, we find that although FMs excel in pattern recognition they fall short on causal reasoning, domain robustness and safety. Clinical translation is hindered by scarce representative data for model training, unverified generalization beyond over-simplified benchmark settings and a lack of prospective outcome-based validation. This Perspective provides clinicians with a practical way to interpret FM claims, identify where these systems may safely support imaging workflows and recognize why human oversight remains indispensable. For developers, it defines the validation, workflow, safety and governance requirements that must be met before FMs can become clinically reliable tools. We envision that the path forward lies not in a monolithic medical oracle, but in coordinated subspecialist AI systems that are transparent, safe and clinically grounded. This is a preview of subscription content, access via your institution Access options Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription $32.99 / 30 days cancel any time Subscribe to this journal Receive 12 digital issues and online access to articles $119.00 per year only $9.92 per issue Buy this article - Purchase on SpringerLink -
Aug 11, 2026 · via nature.com
Two San Francisco bars recently discontinued their use of Patronscan Guard+, an AI-powered facial recognition and surveillance technology that uses cameras and scanners to collect patrons’ personal data, including an image of their face and government-issued ID as well as their names, addresses, genders, and even patron behavior inside the venue. Patronscan is used to flag “blacklisted” customers, track fake IDs, assist with criminal investigations, and alert other companies using its services about flagged patrons. However, opponents of the tech worry the collected data could help empower anti-LGBTQ+ attacks, especially amid the current administration’s persecution of LGBTQ+ people and their allies as terrorist extremists. Related “Effective immediately, we are pausing the use of Patronscan while we review our ID verification and security practices,” Brian Aranda, director of operations at both Toad Hall and Badlands, told the San Francisco Chronicle. “Our security teams will continue to thoroughly check IDs manually during this time. We appreciate everyone who has shared their concerns and remain committed to providing a safe and welcoming environment for all.” Patronscan’s website says it doesn’t sell or share any patron’s personal information with third-party companies. But, despite its assurances that it doesn’t conduct facial recognition analysis and only stores patron’s data for 21 days (unless the patron requests the company to delete it beforehand), the company itself admitted that it saves the data of flagged patrons for anywhere from one to five years. Never Miss a Beat Subscribe to our newsletter to stay ahead of the latest LGBTQ+ political news and insights. “For bar and nightclub clients, we collect: full name, date of birth, gender (if available on ID), zip code (if available on ID), and expiry date (if available on ID). If an additional authenticity check is required, a venue can run a one-time comparison of the
Aug 11, 2026 · via lgbtqnation.com
Twins Test FBI’s Biometrics How studying twins helps the Bureau evaluate and improve AI biometric systems A twin takes part in biometrics screening during the 2026 Twins Days Festival in Twinsburg, Ohio. Every August, thousands of siblings gather in Twinsburg, Ohio, for the annual Twins Days Festival. While the event is primarily a social celebration of multiples from around the world, it also serves as the setting for one of the FBI's most distinctive scientific research efforts. For more than 15 years, a partnership between the Bureau and West Virginia University (WVU) has utilized this gathering to evaluate and improve biometric technologies. Tucked away from the main festivities is an area known to regulars as "Research Row," where white tents stand alongside food trucks and souvenir stands. Here, researchers from a variety of industries invite festival-goers to step out of the summer heat and participate in various studies. The FBI and WVU are a staple there—hosting hundreds of individuals who volunteer to take part in a study designed to answer one of biometric science's most pressing questions: How do you accurately distinguish between two people who look almost exactly alike? The resulting dataset—one of the few of its kind in the world—helps researchers evaluate fingerprint, iris, and facial recognition technologies, improving their accuracy while deepening the understanding of their limitations. The ultimate biometric challenge “From a biometrics perspective, identical twins are the ultimate challenge because we are trying to uniquely identify one person from another,” said Ben Smith, a biometric program manager in the FBI’s Criminal Justice Information Services Division. “When it comes to facial recognition, the identical twins scenario is probably the most difficult challenge because they can look practically the same.” That difficulty is precisely what makes twins so valuable to science. Although identical twins often share remarkably
Aug 11, 2026 · via fbi.gov
Law review: You Think You’re Hot. You Probably Aren’t. License Plate Readers, Hot Lists, and the Fourth Amendment If you drove through Truckee sometime last month, your car was probably photographed by one of the Town’s Flock Safety automated license plate readers (ALPR). Odds are no one ever looked at the picture. Like a dating app, interest exists only if you meet someone else’s criteria. Here, that means your license plate appears on the hot list. If it doesn’t, no one looks at your photo twice. For once, that’s a compliment. That naturally raises three questions. What exactly is in the photograph? Is this legal? And perhaps most importantly: what on earth is a “hot list?” The cameras photograph cars, not people. They capture the rear of the vehicle, read the license plate, and note details such as the make, model, color, roof rack, and bumper stickers. They do not use facial recognition and cannot identify the driver. The system knows a surprising amount about your car and almost nothing about the person driving it. Every license plate is automatically compared against a “hot list” of vehicles associated with stolen cars and AMBER Alerts. If there is a match, an officer is notified and must visually confirm the vehicle before taking any action. If there is no match, no one looks at the image again, and it is automatically deleted after 30 days. According to the Truckee Police Department, auto thefts increased 300 percent in 2022. Truckee researched ALPR technology, secured grant funding, and launched a 17-camera pilot program in July 2023. Since then, the department credits the cameras with helping identify homicide suspects, missing persons, burglary suspects, and fraud offenders. One homicide suspect tried escaping through Tahoe Donner’s back roads but was quickly located after an ALPR alert. During
Aug 11, 2026 · via sierrasun.com
Immigration and Customs Enforcement (ICE) plans to pay LexisNexis millions more dollars for continued access to data it can feed into a Palantir platform, according to newly published procurement records. The data is to help, among other sections, ICE’s Enforcement and Removal Operations (ERO), the part of ICE focused on deportation. The documents provide some more insight into the sort of information ICE intends to use Palantir to process. In January, 404 Media revealed the existence of ELITE, a system Palantir made for ICE that helps the agency find which neighborhoods to target by bringing together data from private suppliers and government agencies. The news comes as ICE arrested more than 51,000 people in July, many of those at airports. The data is to support “all aspects of ICE screening and vetting, lead development, and criminal analysis activities,” one of the documents says. That also includes identifying what ICE perceives as fraud “before crime and fraud can materialize and detecting and reporting elements of crimes involving the exploitation or attempts to exploit the immigration and customs laws of the United States.” ICE anticipates paying LexisNexis $6.7 million, according to the records. LexisNexis has long provided data to ICE across multiple administrations. In this new procurement record, ICE is looking to continue access to “to LexID and Accurint Virtual Crime Center,” according to a listing posted along with the document. LexID and Accurint are LexisNexis tools that take a wealth of data — “over 82 billion public and proprietary records from more than 10,000 sources” according to the company’s website — and link them together to help law enforcement locate people. The document suggests ICE wants to pair these capabilities and data with Palantir systems, too. One part reads, “The government's requirement is that the database must be able to application
Aug 11, 2026 · via 404media.co
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Aug 11, 2026 · via facebook.com
Mayflower has become the second Central Arkansas city in the last week to ditch its Flock Safety license plate readers. Interim Police Chief Brittany Byrd announced Monday that after “evaluating the capabilities” of the cameras, Mayflower will discontinue their use and will “rescind their deployment within the city immediately.” The move comes three days after the city of Cabot announced its decision to stop using Flock cameras pending a “comprehensive review.” Last month, the police chief in Pea Ridge announced his city would no longer use the devices. In her announcement, Byrd acknowledged Flock cameras have aided in the investigation of criminal cases across the country, including in Mayflower. But despite the benefits, she wrote, “we believe them to be intrusive in nature.” “The Mayflower Police Department will not participate in tactics which compromise or infringe upon the liberties entitled to law-abiding citizens,” Byrd wrote. Flock license plate reader cameras take pictures of passing cars and log their license plate numbers, comparing the captured information against so-called hot lists assembled by the law enforcement agencies that operate them. Flock Safety, the company that manufactures the devices, states on its website that the cameras don’t use facial recognition software but boast the ability to search captured images based on a description of a vehicle — such as its color, make and model, or unique features such as bumper stickers — if a plate number isn’t known. Many cities in Arkansas use the cameras, including Little Rock, North Little Rock, Conway, Hot Springs, Benton, Bryant, Jacksonville and Sherwood. Nationally, more than 5,000 law enforcement agencies have bought Flock’s license plate readers, the company’s website states. Byrd said Mayflower signed a multi-year agreement with Flock Safety in 2023 that runs through 2027. “Police Department personnel have been communicating with representatives from Flock Safety
Aug 10, 2026 · via arkansasonline.com
Two bars in San Francisco's Castro District are pausing the use of facial recognition technology to verify identities following community backlash.
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Aug 10, 2026 · via ktvu.com
Windows 11 is pulling picture passwords and pushing people to PINs, passwords, and biometrics Microsoft is phasing out picture passwords in favour of more secure methods. Windows 11 will soon lose a way to log in to a PC. A recent update removes the option to set up a new picture password. If you already have a picture password on your PC, you can continue to use it, but the feature is being phased out. Microsoft is on a mission to push the world beyond passwords. The tech giant generally recommends a PIN, facial recognition, or a fingerprint to log in to Windows 11. Passkeys are often the top recommendation for authentication, though you cannot log in to a Windows 11 PC with a passkey. But back in the days of Windows 8, Microsoft introduced the option to unlock your PC using a picture. That feature, which has remained available across Windows 10 and Windows 11, allows you to unlock your PC with a series of touches or gestures, like tapping the eyes of a smiley face then swiping across the face's mouth. You could use a combination of three types of gestures as part of a picture password: circles, straight lines, or taps. Picture passwords were a fun way to show off the touch-centric nature of Windows 8, but they aren't as secure as a passkey or even a strong password. Since people often pick gestures or taps around key objects, hackers can sometimes guess the gestures needed to unlock a PC. People can look at the smudges on your screen to guess where you touch frequently. A research paper from 2013 that was flagged by Neowin highlights the risks of picture passwords. If you have a picture password set up now and want to continue to use it,
Aug 10, 2026 · via windowscentral.com
British Transport Police (BTP) will begin using live facial recognition technology at Transport for London (TfL) Underground stations as part of an ongoing trial. The first deployment will take place at Victoria Underground station on Tuesda (August 11) as part of an extended pilot now running until November. It will then rotate between deployments at Underground and Network Rail stations. Chief Superintendent Chris Casey, BTP's senior officer responsible for the project, said: "This marks an important new stage in our trial of Live Facial Recognition technology. "Expanding deployments into London Underground stations will help us assess the technology in a different transport environment while continuing to refine how it is used across the railway network. "Our deployments are intelligence-led and focused on identifying people wanted by the police or courts, including those suspected of serious offences and those who may be breaching bail conditions or court orders. "Our focus remains on protecting the public, preventing crime and bringing offenders to justice, while ensuring the technology is used lawfully, proportionately and transparently." The cameras work by scanning faces and comparing them to a watchlist of people who are wanted by the police or courts, or who are subject to conditions designed to protect the public. Read more - 1,500 London bus drivers to strike - more than 40 routes affected - London's cleanest and dirtiest boroughs for food hygiene have been revealed - This student is giving away free coffee on the London Underground If a match is found, an alert will be triggered and reviewed by an officer before any action is taken. Siwan Hayward, Director of Security, Policing and Enforcement at TfL, said: "Live facial recognition has the potential to be a powerful additional tool in helping police quickly identify those wanted for high-harm offences, including sexual offences, and
Aug 10, 2026 · via yourlocalguardian.co.uk
Privacy concerns raised over Australian-first live AI police face scanning trial An Australian-first trial of live AI facial recognition technology by a police force has been criticised by privacy experts and legal advocates. The mass AI surveillance pilot has been rolled out in Western Australia and is being closely watched by law enforcement agencies across the country. Since the trial began in June, cameras linked to facial recognition software have scanned more than 130,000 people in Perth and Fremantle, comparing their faces against a police watchlist in real time. Western Australian police say the results are promising but questions have been raised about an opaque watchlist and trial site locations that some say may target First Nations people. In June, the Western Australian Police announced it would be the first police force to use live facial recognition technology on the public. Facial recognition technology has become increasingly commonplace. It includes simpler one-to-one systems that check whether a single face matches a particular identity, like those used to unlock a mobile phone. It also includes the more complex one-to-many systems like those used in Bunnings to compare faces of customers against a database of people suspected of activity including theft or abuse of staff. Since June, WA Police have been trialling that more complex type of system. Using tech company NEC's Neoface m40 product, a pilot program scanned more than 130,000 faces around Perth and Fremantle in the first week of the trial. It compared their faces against a watchlist of about 4,000 people, including people accused of serious offences, people who are missing, and people who may pose a risk to themselves or others, police said. WA Police celebrated the early results from the trial, which resulted in 33 alerts and 18 arrests. Of the faces scanned in the first
Aug 10, 2026 · via abc.net.au