The proposed class action alleges Meta illegally harvested people’s Facebook and Instagram photos to train its AI image-generation models and to build its unreleased “NameTag” face recognition feature.
Meta’s going to put AI-generated images in your Facebook and Instagram feeds
Facebook and Instagram feeds are about to get messier.
Here’s the Truth About Whether Meta’s NameTag Face Recognition Tech ‘Exists’
Since WIRED reported on Meta’s NameTag face recognition system, company executives have made confusing and conflicting remarks about its very existence.
Facebook and Twitter shutter pro-Trump network reaching 55 million accounts
They used AI-generated faces as profile photos
In China, people are renting out their faces to AI
New platforms are paying people to license their likeness for AI-generated dramas and ads, creating a new marketplace for biometric identity.
Hackers Simply Asked Meta AI to Give Them Access to High-Profile Instagram Accounts. It Worked
The exploit shows the extreme risk of offloading technical support to AI.
Meta plans to add facial recognition to its smart glasses, report claims | TechCrunch
The feature, internally known as “Name Tag,” would allow smart glasses wearers to identify people and get information about them via Meta's AI assistant.
Exploring Frequency Adversarial Attacks for Face Forgery Detection
Various facial manipulation techniques have drawn seri-ous public concerns in morality, security, and privacy. Al- though existing face forgery classifiers achieve promising performance on detecting fake images, these methods are vulnerable to adversarial examples with injected impercep- tible perturbations on the pixels. Meanwhile, many face forgery detectors always utilize the frequency diversity be-tween real and fake faces as a crucial clue. In this paper, in- stead of injecting adversarial perturbations into the spatial domain, we propose a frequency adversarial attack method against face forgery detectors. Concretely, we apply dis-crete cosine transform (DCT) on the input images and in-troduce a fusion module to capture the salient region of ad-versary in the frequency domain. Compared with existing adversarial attacks (e.g. FGSM, PGD) in the spatial do-main, our method is more imperceptible to human observers and does not degrade the visual quality of the original images. Moreover, inspired by the idea of meta-learning, we also propose a hybrid adversarial attack that performs at-tacks in both the spatial and frequency domains. Exten-sive experiments indicate that the proposed method fools not only the spatial-based detectors but also the state-of- the-art frequency-based detectors effectively. In addition, the proposed frequency attack enhances the transferability across face forgery detectors as black-box attacks.
Exploring Frequency Adversarial Attacks for Face Forgery Detection
Various facial manipulation techniques have drawn seri-ous public concerns in morality, security, and privacy. Al- though existing face forgery classifiers achieve promising performance on detecting fake images, these methods are vulnerable to adversarial examples with injected impercep- tible perturbations on the pixels. Meanwhile, many face forgery detectors always utilize the frequency diversity be-tween real and fake faces as a crucial clue. In this paper, in- stead of injecting adversarial perturbations into the spatial domain, we propose a frequency adversarial attack method against face forgery detectors. Concretely, we apply dis-crete cosine transform (DCT) on the input images and in-troduce a fusion module to capture the salient region of ad-versary in the frequency domain. Compared with existing adversarial attacks (e.g. FGSM, PGD) in the spatial do-main, our method is more imperceptible to human observers and does not degrade the visual quality of the original images. Moreover, inspired by the idea of meta-learning, we also propose a hybrid adversarial attack that performs at-tacks in both the spatial and frequency domains. Exten-sive experiments indicate that the proposed method fools not only the spatial-based detectors but also the state-of- the-art frequency-based detectors effectively. In addition, the proposed frequency attack enhances the transferability across face forgery detectors as black-box attacks.
Worried About Meta Using Your Instagram to Train Its A.I.? Here’s What to Know.
Social media users voiced worries about a move by Meta to use information from public posts, including on Facebook, to train its chatbot.
AI-synthesized faces are indistinguishable from real faces and more trustworthy
Artificial intelligence (AI)–synthesized text, audio, image, and video are being weaponized for the purposes of nonconsensual intimate imagery, financial fraud, and disinformation campaigns. Our evaluation of the photorealism of AI-synthesized faces indicates that synthesis engines have passed through the uncanny valley and are capable of creating faces that are indistinguishable—and more trustworthy—than real faces.
AI-synthesized faces are indistinguishable from real faces and more trustworthy
Artificial intelligence (AI)–synthesized text, audio, image, and video are being weaponized for the purposes of nonconsensual intimate imagery, financial fraud, and disinformation campaigns. Our evaluation of the photorealism of AI-synthesized faces indicates that synthesis engines have passed through the uncanny valley and are capable of creating faces that are indistinguishable—and more trustworthy—than real faces.
Identifying AI-generated images with SynthID
Today, in partnership with Google Cloud, we’re beta launching SynthID, a new tool for watermarking and identifying AI-generated images. It’s being released to a limited number of Vertex AI customers using Imagen, one of our latest text-to-image models that uses input text to create photorealistic images. This technology embeds a digital watermark directly into the pixels of an image, making it imperceptible to the human eye, but detectable for identification. While generative AI can unlock huge creative potential, it also presents new risks, like creators spreading false information — both intentionally or unintentionally. Being able to identify AI-generated content is critical to empowering people with knowledge of when they’re interacting with generated media, and for helping prevent the spread of misinformation.
Someone Put Facial Recognition Tech onto Meta's Smart Glasses to Instantly Dox Strangers
The technology, which marries Meta’s smart Ray Ban glasses with the facial recognition service Pimeyes and some other tools, lets someone automatically go from face, to name, to phone number, and home address.
Meta spotted testing AI-generated comments on Instagram | TechCrunch
In recent years, Meta has introduced many AI features and capabilities to its apps, even going so far as experimenting with AI-generated characters
Meta Silently Added Face-Recognition Code for Its Smart Glasses to Millions of Phones
Code reviewed by WIRED uncovered an unreleased face-recognition system embedded in Meta’s smart glasses platform. It’s designed to identify people via biometric data stored on users’ phones.
