Married doctor's affair with glamorous younger woman explodes into Fatal Attraction-style court war... X-rated photo claims, leaked recordings and a sinister threat: 'I'll never stop' NBA rocked as Grizzlies star Brandon Clarke dies suddenly at 29... a month after being arrested on drug charges The unassuming apps all cheaters use to hide their affairs: Where to look on your partner's phone to see exactly what they are up to... and the subtle red flags to never ignore I've treated so many cocaine users. This is the one sign that makes it so obvious you have a problem, how it can kill you in a night... and the embarrassing sexual side effect you may not have heard of: DR PHILIPPA KAYE Explosive Supreme Court LEAK reveals stinging whispers about'belligerent' justice read the wild rants troubling both sides of the aisle Surge in cancer patients taking 20 cent'wonder drug' after Mel Gibson claims that friends beat incurable disease thanks to drug The'marry me' sex move that'll make even the most commitment-phobic of men beg to see you again... and it worked for THREE of my friends Trump's chilling'treason' note revealed as he hunts down Iran war leakers... and Israel bombshell sparks fury Hollywood's $350k matchmaker exposes the secret love lives of the rich and famous: Diva demands, fake names, NDAs... and how to know if your relationship is doomed Secret trove of injury photos that blow apart married tech mogul's family-man image revealed in explosive lawsuit: Bruises, beatings and forced sex acts he allegedly inflicted on girlfriend Furious argument explodes on CNN after panelist flagged Kevin O'Leary's old age during foul-mouthed fight about politics He knew Elizabeth Taylor's secrets. Johnny Depp came to him for answers. But Hollywood's greatest confidante buried a betrayal that was too dangerous to expose Experts issue urgent warning over doing a'peace' sign in photos - amid fears hackers can steal your FINGERPRINTS and copy them Your latest selfie could be giving hackers everything they need to crack your accounts, experts have warned. Cybersecurity researchers have issued an urgent warning against doing a'peace' sign in photos, amid fears that criminals could steal your fingerprints.

Oura just announced a couple of new features that keep an eye on hormonal health for women. The pre-existing Cycle Insights feature, which tracks menstrual cycles, will now take hormonal birth control methods into consideration. The smart ring maker says that this first-of-its-kind experience will help users see how these methods can impact overall biometric data. This has been designed to provide personalized guidance during complex hormonal changes, so it can integrate data from over 20 combinations of birth control methods. These include pills, patches, IUDs and implants.

We developed a robot arm that collaboratively paints with a human artist. The robot has an awareness of the artist's heartbeat through the EmotiBit sensor, which provides the arousal levels of the painter . Given the heartbeat detected, the robot decides to increase proximity to the artist's workspace or retract. If a higher heartbeat is detected, which is associated with increased arousal in human artists, the robot will move away from that area of the canvas. If the artist's heart rate is detected as neutral, indicating the human artist's baseline state, the robot will continue its painting actions across the entire canvas. We also demonstrate and propose alternative robot-artist interactions using natural language and physical touch. This work combines the biometrics of a human artist to inform fluent artistic interactions.

Because biometric data is sensitive, centralized training poses a privacy risk, even though biometric recognition is essential for contemporary applications. Federated learning (FL), which permits decentralized training, provides a privacy-preserving substitute. Conventional FL, however, has trouble with interpretability and heterogeneous data (non-IID). In order to handle non-IID biometric data, this framework adds an attention mechanism at the central server that weights local model updates according to their significance. Differential privacy and secure update protocols safeguard data while preserving accuracy. The A3-FL framework is evaluated in this study using FVC2004 fingerprint data, with each client's features extracted using a Siamese Convolutional Neural Network (Siamese-CNN). By dynamically modifying client contributions, the attention mechanism increases the accuracy of the global model.The accuracy, convergence speed, and robustness of the A3-FL framework are superior to those of standard FL (FedAvg) and static baselines, according to experimental evaluations using fingerprint data (FVC2004). The accuracy of the attention-based approach was 0.8413, while FedAvg, Local-only, and Centralized approaches were 0.8164, 0.7664, and 0.7997, respectively. Accuracy stayed high at 0.8330 even with differential privacy. A scalable and privacy-sensitive biometric system for secure and effective recognition in dispersed environments is presented in this work.

Artificial Intelligence have profoundly transformed the technological landscape in recent years. Large Language Models (LLMs) have demonstrated impressive abilities in reasoning, text comprehension, contextual pattern recognition, and integrating language with visual understanding. While these advances offer significant benefits, they also reveal critical limitations in the models' ability to grasp the notion of privacy. There is hence substantial interest in determining if and how these models can understand and enforce privacy principles, particularly given the lack of supporting resources to test such a task. In this work, we address these challenges by examining how legal frameworks can inform the capabilities of these emerging technologies. T o this end, we introduce a comprehensive, multilevel Visual Privacy T axonomy that captures a wide range of privacy issues, designed to be scalable and adaptable to existing and future research needs. Furthermore, we evaluate the capabilities of several state-of-the-art Vision-Language Models (VLMs), revealing significant inconsistencies in their understanding of contextual privacy. Our work contributes both a foundational taxonomy for future research and a critical benchmark of current model limitations, demonstrating the urgent need for more robust, privacy-aware AI systems.

Kurt Knutsson reports World ID's iris scanning tech launches in six U.S. cities to verify identity, fight AI bots. OpenAI CEO Sam Altman, known for creating ChatGPT, has launched World, a project that uses an eye scan to prove you are a real person online. The idea is to help people stand out from bots and AI by creating a digital ID with a quick scan from a device called the Orb. While Altman says this technology keeps humans central as AI advances, it also raises serious concerns about privacy and the security of sensitive biometric data, with critics and regulators questioning how this information will be used and protected. Join the FREE "CyberGuy Report": Get my expert tech tips, critical security alerts and exclusive deals, plus instant access to my free "Ultimate Scam Survival Guide" when you sign up! World ID relies on a device called the Orb, a spherical scanner that captures a person's iris pattern to generate a unique IrisCode.

In this study, we introduce a novel multi-modal biometric authentication system that integrates facial, vocal, and signature data to enhance security measures. Utilizing a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), our model architecture uniquely incorporates dual shared layers alongside modality-specific enhancements for comprehensive feature extraction. The system undergoes rigorous training with a joint loss function, optimizing for accuracy across diverse biometric inputs. Feature-level fusion via Principal Component Analysis (PCA) and classification through Gradient Boosting Machines (GBM) further refine the authentication process. Our approach demonstrates significant improvements in authentication accuracy and robustness, paving the way for advanced secure identity verification solutions.

Deep learning is actively being used in biometrics to develop efficient identification and verification systems. Handwritten signatures are a common subset of biometric data for authentication purposes. Generative adversarial networks (GANs) learn from original and forged signatures to generate forged signatures. While most GAN techniques create a strong signature verifier, which is the discriminator, there is a need to focus more on the quality of forgeries generated by the generator model. This work focuses on creating a generator that produces forged samples that achieve a benchmark in spoofing signature verification systems. We use CycleGANs infused with Inception model-like blocks with attention heads as the generator and a variation of the SigCNN model as the base Discriminator. We train our model with a new technique that results in 80% to 100% success in signature spoofing. Additionally, we create a custom evaluation technique to act as a goodness measure of the generated forgeries. Our work advocates generator-focused GAN architectures for spoofing data quality that aid in a better understanding of biometric data generation and evaluation.

Apple has been using your face data for security for seven years. You likely use your fingerprint to unlock at least a few of your devices. But have you paid with your palm at Whole Foods yet? Did the TSA scan your face the last time you were at the airport? Using biometric info like your fingerprint and face can save a little time, but a whole lot of potential security risks come along for the ride.

The recent rapid advancements in both sensing and machine learning technologies have given rise to the universal collection and utilization of people's biometrics, such as fingerprints, voices, retina/facial scans, or gait/motion/gestures data, enabling a wide range of applications including authentication, health monitoring, or much more sophisticated analytics. While providing better user experiences and deeper business insights, the use of biometrics has raised serious privacy concerns due to their intrinsic sensitive nature and the accompanying high risk of leaking sensitive information such as identity or medical conditions. In this paper, we propose a novel modality-agnostic data transformation framework that is capable of anonymizing biometric data by suppressing its sensitive attributes and retaining features relevant to downstream machine learning-based analyses that are of research and business values. We carried out a thorough experimental evaluation using publicly available facial, voice, and motion datasets. Results show that our proposed framework can achieve a \highlight{high suppression level for sensitive information}, while at the same time retain underlying data utility such that subsequent analyses on the anonymized biometric data could still be carried out to yield satisfactory accuracy.