The people-search tool ClarityCheck says its reverse image search service is "private and secure"--but it left a database containing more than 9 million image files exposed. When someone uploads a photo to the people-search tool ClarityCheck, the website has a clear message: "Your reverse image search is private and secure." New research, though, shows that the website left more than 9 million image files, including photographs of people's faces, publicly exposed . And a second misconfiguration publicly exposed people's email addresses and phone numbers. Overall, according to findings from independent security researcher Jeremiah Fowler, the exposed ClarityCheck database contained roughly 450 GB of images, including what appeared to be profile images, screenshots, and other photographs of adults, teenagers, and children.

Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series More than 4.6 million license plates appear in the website's collection of public Flock records. Olivia Tauber is the deputy editor of digital culture, covering creators, media, movies, beauty, and more. Based in New York, her work has appeared in The New York Times, Vanity Fair, The Cut, Teen Vogue, Complex, and Interview Magazine. She holds a Master's degree in Journalism from NYU and a Bachelor's from the University of Michigan. She also runs Fan Mail, a weekly pop-culture newsletter.

Safety Net Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Creator Hub Versus Say More Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series Flock just announced new default privacy settings. Chase joined Mashable's Social Good team in 2020, covering online stories about digital activism, climate justice, accessibility, and media representation. Civil rights groups question the new policy shift. Controversial surveillance technology company Flock Safety has unveiled sweeping new privacy guardrails, seemingly intended to quell growing unrest over its devices' presence across the country. Days prior, CEO Garrett Langley went on a media run to convince the public that the company was not the latest cog in a dystopian surveillance state, but rather a purveyor of public safety.

US Customs and Border Protection (CBP) agents have reportedly misused electronic databases to spy on family members, try to get dates and even provide intelligence to suspected drug traffickers, according to freedom of information (FOIA) files seen by Wired. Officers allegedly abused databases that can draw from sources like license plate readers, facial recognition and smartphone searches to violate the privacy of numerous individuals. A CBP officer is alleged to have used a government database to contact a flight attendant, and another was accused of using data from trusted-traveler applications to ask people out. One employee provided border-crossing data to someone involved in a divorce, and another abused internal policies by tracking coworkers cellphones with ad-tech-derived location data. Of 300 incidents tracked by Wired, 138 were referred to CBP management and 78 assigned to criminal investigators, while 43 others weren't investigated.

Records obtained by WIRED detail hundreds of allegations of Customs and Border Protection workers misusing internal tools to look up romantic interests and track colleagues' cell phones. Internal records obtained by WIRED reveal how, for years, United States Customs and Border Protection employees and contractors were accused of abusing sensitive government databases for reasons that had nothing to do with their jobs. The records contain hundreds of allegations of misuse of law enforcement databases, including federal agents querying data to look up romantic interests, monitor family members, expose various personal information and, in some cases, provide intelligence to suspected smugglers or drug-trafficking organizations. Acquired through Freedom of Information Act requests to CBP's Office of Professional Responsibility and the Department of Homeland Security's Office of Inspector General, the records reveal the breadth of alleged database abuse by CBP employees spanning more than a decade. As immigration and border authorities expand their surveillance through facial recognition, license plate readers, mobile-device searches, and commercially purchased location information generated by ordinary apps, the sheer range of these records, which date from 2009 through 2022, highlights how US residents can be--and have been--targeted by federal government employees with access to highly sensitive data and powerful tools.

PCWorld reports that PC case clearance specs from manufacturers are often inaccurate, with Noctua testing over 100 cases and finding 56 with significant measurement errors. Brands like Asus and Lian-Li offer less clearance than officially listed, while some Corsair cases actually provide more space, risking incompatible builds or unnecessary returns. Noctua launched a database with accurate clearance data to help builders verify dimensions before purchasing components. Detailed instructions are provided below. PC builders know that millimeters matter.

Since WIRED reported on Meta's NameTag face recognition system, company executives have made confusing and conflicting remarks about its very existence. Does a software feature exist if its code has been deployed to the devices of millions of people but they can't use it yet? Not if you work at Meta . The company's executives have spent the last few weeks making this semantic argument about NameTag, the in-development face-recognition system that Meta built for its smart glasses . The inevitable result is confusion, but that's easy enough to clear up.

To address this problem, fine-tuning longcontext LVLMs and employing GPT-based agents have emerged as promising solutions. However, fine-tuning LVLMs would require extensive high-quality data and substantial GPU resources, while GPT-based agents would rely on proprietary models (e.g., GPT-4o). In this paper, we propose Video Retrieval-Augmented Generation (Video-RAG), a training-free and cost-effective pipeline that employs visually-aligned auxiliary texts to help facilitate cross-modality alignment while providing additional information beyond the visual content. Specifically, we leverage open-source external tools to extract visually-aligned information from pure video data (e.g., audio, optical character, and object detection), and incorporate the extracted information into an existing LVLM as auxiliary texts, alongside video frames and queries, in a plug-and-play manner. Our Video-RAG offers several key advantages: (i) lightweight with low computing overhead due to singleturn retrieval; (ii) easy implementation and compatibility with any LVLM; and (iii) significant, consistent performance gains across long video understanding benchmarks, including Video-MME, MLVU, and LongVideoBench. Notably, our model demonstrates superior performance over proprietary models like Gemini1.5-Pro and GPT-4o when utilized with a 72B model.

Building generative models for relational databases (RDBs) is important for many applications, such as privacy-preserving data release and augmenting real datasets. However, most prior works either focus on single-table generation or adapt singletable models to the multi-table setting by relying on autoregressive factorizations and sequential generation. These approaches limit parallelism, restrict flexibility in downstream applications, and compound errors due to commonly made conditional independence assumptions. In this paper, we propose a fundamentally different approach: jointly modeling all tables in an RDB without imposing any table order. By using a natural graph representation of RDBs, we propose the Graph-Conditional Relational Diffusion Model (GRDM), which leverages a graph neural network to jointly denoise row attributes and capture complex inter-table dependencies. Extensive experiments on six real-world RDBs demonstrate that our approach substantially outperforms autoregressive baselines in modeling multi-hop inter-table correlations and achieves state-of-the-art performance on single-table fidelity metrics.

We propose LiteReality, a novel pipeline that converts RGB-D scans of indoor environments into compact, realistic, and interactive 3D virtual replicas. LiteReality not only reconstructs scenes that visually resemble reality but also supports key features essential for graphics pipelines--such as object individuality, articulation, high-quality physically based rendering materials. At its core, LiteReality first performs scene understanding and parses the results into a coherent 3D layout and objects, with the help of a structured scene graph.