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Daily Archives: January 1, 2020
Government exposes addresses of > 1000 new year honours recipients
More than 1,000 celebrities, government employees and politicians who have received honours had their home and work addresses posted on a government website, the Guardian can reveal.
The accidental disclosure of the tranche of personal details is likely to be considered a significant security breach, particularly as senior police and Ministry of Defence staff were among those whose addresses were made public.
Many of the more than a dozen MoD employees and senior counter-terrorism officers who received honours in the new year list had their home addresses revealed in a downloadable list, along with countless others who may believe the disclosure has put them in a vulnerable position.
Prominent public figures including the musician Elton John, the cricketer Ben Stokes, NHS England’s chief executive, Simon Stevens, the politicians Iain Duncan Smith and Diana Johnson, TV chef Nadiya Hussain, and the former director of public prosecutions Alison Saunders were among those whose home addresses were published.
Others included Jonathan Jones, the permanent secretary of the government’s legal department, and John Manzoni, the Cabinet Office permanent secretary. Less well-known figures included academics, Holocaust survivors, prison staff and community and faith leaders.
It is thought the document seen by the Guardian, which contains the details of 1,097 people, went online at 10.30pm on Friday and was taken down in the early hours of Saturday.
The vast majority of people on the list had their house numbers, street names and postcodes included.
Source: Government exposes addresses of new year honours recipients | UK news | The Guardian
Wyze data leak may have exposed personal data of millions of users
Security camera startup Wyze has confirmed it suffered a data leak this month that may have left the personal information of millions of its customers exposed on the internet. No passwords or financial information were exposed, but email addresses, Wi-Fi network IDs and body metrics were left unprotected from Dec. 4 through Dec. 26, the company said Friday.
More than 2.4 million Wyze customers were affected by the leak, according to cybersecurity firm Twelve Security, which first reported on the leak
“We are still looking into this event to figure out why and how this happened,” he wrote.
In an update Sunday, Song said Wyze discovered a second unprotected database during its investigation of the data leak. It’s unclear what information was stored in this database, but Song said passwords and personal financial data weren’t included.
Source: Wyze data leak may have exposed personal data of millions of users – CNET
Researchers detail AI that de-hazes and colorizes underwater photos
Ever notice that underwater images tend to be be blurry and somewhat distorted? That’s because phenomena like light attenuation and back-scattering adversely affect visibility. To remedy this, researchers at Harbin Engineering University in China devised a machine learning algorithm that generates realistic water images, along with a second algorithm that trains on those images to both restore natural color and reduce haze. They say that their approach qualitatively and quantitatively matches the state of the art, and that it’s able to process upwards of 125 frames per second running on a single graphics card.
The team notes that most underwater image enhancement algorithms (such as those that adjust white balance) aren’t based on physical imaging models, making them poorly suited to the task. By contrast, this approach taps a generative adversarial network (GAN) — an AI model consisting of a generator that attempts to fool a discriminator into classifying synthetic samples as real-world samples — to produce a set of images of specific survey sites that are fed into a second algorithm, called U-Net.
The team trained the GAN on a corpus of labeled scenes containing 3,733 images and corresponding depth maps, chiefly of scallops, sea cucumbers, sea urchins, and other such organisms living within indoor marine farms. They also sourced open data sets including NY Depth, which comprises thousands of underwater photographs in total.
Post-training, the researchers compared the results of their twin-model approach to that of baselines. They point out that their technique has advantages in that it’s uniform in its color restoration, and that it recovers green-toned images well without destroying the underlying structure of the original input image. It also generally manages to recover color while maintaining “proper” brightness and contrast, a task at which competing solutions aren’t particularly adept.
It’s worth noting that the researchers’ method isn’t the first to reconstruct frames from damaged footage. Cambridge Consultants’ DeepRay leverages a GAN trained on a data set of 100,000 still images to remove distortion introduced by an opaque pane of glass, and the open source DeOldify project employs a family of AI models including GANs to colorize and restore old images and film footage. Elsewhere, scientists at Microsoft Research Asia in September detailed an end-to-end system for autonomous video colorization; researchers at Nvidia last year described a framework that infers colors from just one colorized and annotated video frame; and Google AI in June introduced an algorithm that colorizes grayscale videos without manual human supervision.
Source: Researchers detail AI that de-hazes and colorizes underwater photos