Google's Pixel phones are chock-full of helpful, smart features, and they capture some of the best-looking photographs on a mobile device. But like several of its peers, Google doesn't have many of its best features turned on by default. For example, Call Screening blocks unwanted phone calls on your behalf, and you need to turn it on yourself. I test smartphones for a living, and I'm constantly switching to a new device every week or two. I'm an expert at running through the settings of every phone I test and toggling on the features I want.

Apple's software design strives to be intuitive, but each iteration of iOS contains so many additions and tweaks that it's easy to miss some useful iPhone settings. Apple focused on artificial intelligence when it unveiled iOS 18 in 2024, but there's a lot under the hood that adds to the steady stream of intriguing customizations and lesser-known features from the last few years. Several helpful settings are turned off by default, and it's not immediately obvious how to switch off some annoying features. We're here to help you get the most out of your Apple phone. Once you have things set up the way you want, it's a breeze to copy everything, including settings, when you switch to a new iPhone.

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These settings allow your iPhone to share data that helps third parties target advertisements to you and measure advertisement engagement. Chip Hallett, author of The Ultimate Privacy Playbook, explained how to turn these'dangerous' settings off to ensure that your data is always kept private. To disable them, start by opening the settings app. Then scroll down and tap'Safari.' Then scroll all the way down to the bottom of the screen where it says'Advanced.' Tap this tab, and you should see a toggle on/off button next to'Privacy Preserving Ad Measurement.'

If you've ever got an advert on social media for something you were just talking about, it might be more than an uncanny coincidence. Thanks to virtual assistants like Siri and Alexa, your smartphone isconstantly listening to everything you say. Worryingly, as long as you have consented to the terms and conditions, there is nothing illegal about using that data to bombard you with hyper-specific adverts. Luckily, experts at NordVPN have devised a simple test to work out if your phone is really eavesdropping on your conversations. By deliberately discussing random topics within earshot of your phone, you can see how long it takes for these subjects to appear in your social media feeds.

Deep neural networks (DNNs) are valuable assets, yet their public accessibility raises security concerns about parameter extraction by malicious actors. Recent work by Carlini et al. (crypto'20) and Canales-Mart\'inez et al. (eurocrypt'24) has drawn parallels between this issue and block cipher key extraction via chosen plaintext attacks. Leveraging differential cryptanalysis, they demonstrated that all the weights and biases of black-box ReLU-based DNNs could be inferred using a polynomial number of queries and computational time. However, their attacks relied on the availability of the exact numeric value of output logits, which allowed the calculation of their derivatives. To overcome this limitation, Chen et al. (asiacrypt'24) tackled the more realistic hard-label scenario, where only the final classification label (e.g., "dog" or "car") is accessible to the attacker. They proposed an extraction method requiring a polynomial number of queries but an exponential execution time. In addition, their approach was applicable only to a restricted set of architectures, could deal only with binary classifiers, and was demonstrated only on tiny neural networks with up to four neurons split among up to two hidden layers. This paper introduces new techniques that, for the first time, achieve cryptanalytic extraction of DNN parameters in the most challenging hard-label setting, using both a polynomial number of queries and polynomial time. We validate our approach by extracting nearly one million parameters from a DNN trained on the CIFAR-10 dataset, comprising 832 neurons in four hidden layers. Our results reveal the surprising fact that all the weights of a ReLU-based DNN can be efficiently determined by analyzing only the geometric shape of its decision boundaries.

Influencer marketing remains largely undisclosed or inappropriately disclosed on social media. Non-disclosure issues have become a priority for national and supranational authorities worldwide, who are starting to impose increasingly harsher sanctions on them. This paper proposes a transparent methodology for measuring whether and how influencers comply with disclosures based on legal standards. We introduce a novel distinction between disclosures that are legally sufficient (green) and legally insufficient (yellow). We apply this methodology to an original dataset reflecting the content of 150 Dutch influencers publicly registered with the Dutch Media Authority based on recently introduced registration obligations. The dataset consists of 292,315 posts and is multi-language (English and Dutch) and cross-platform (Instagram, YouTube and TikTok). We find that influencer marketing remains generally underdisclosed on social media, and that bigger influencers are not necessarily more compliant with disclosure standards.

As I mentioned the other week, I've been playing through a PlayStation 1-style, low-poly horror game called Crow Country. They're too intense, and full of unpleasant surprises – I even played The Last of Us with a text walkthrough to tell me when the fungal zombies were going to appear. For last year's critical darling Alan Wake 2, I recruited my partner so I could hand over the controller whenever I felt like something was about to jump out at me. Like Alan Wake 2, a section of Crow Country is set in an abandoned theme park – a well-worn horror setting (Max Payne did it too, as did Left 4 Dead), but one that still reliably freaks me out. Unlike Alan Wake 2, I didn't need my partner to shield my eyes.

Microsoft appears to have hidden its next AI-powered enhancement within a test build of Windows 11: Automatic Super Resolution, which could be Microsoft's answer to image-enhancement technologies like Nvidia's DLSS. It seems just as likely, however, to be an overarching control, like the Dynamic Lighting feature. Twitter user @PhantomofEarth was among the first to report the new feature this week, hidden away in Windows 11 Build 26052. That build right now appears in both the Canary and Dev Channels of Microsoft's Windows 11 Insider channels, and comes with the caveat that Microsoft may never release this specific feature to the general public. Given its focus on AI, though, it appears likely.