As AI-generated image (AIGI) methods become more powerful and accessible, it has become a critical task to determine if an image is real or AI-generated. Because AIGI lack the signatures of photographs and have their own unique patterns, new models are needed to determine if an image is AI-generated. In this paper, we investigate the ability of the Contrastive Language-Image Pre-training (CLIP) architecture, pre-trained on massive internet-scale data sets, to perform this differentiation. We fine-tune CLIP on real images and AIGI from several generative models, enabling CLIP to determine if an image is AI-generated and, if so, determine what generation method was used to create it. We show that the fine-tuned CLIP architecture is able to differentiate AIGI as well or better than models whose architecture is specifically designed to detect AIGI. Our method will significantly increase access to AIGI-detecting tools and reduce the negative effects of AIGI on society, as our CLIP fine-tuning procedures require no architecture changes from publicly available model repositories and consume significantly less GPU resources than other AIGI detection models.

This paper presents gnss lib py, a Python library used to parse, analyze, and visualize data from a variety of GNSS (Global Navigation Satellite Systems) data sources. The gnss lib py library's ease of use, modular capabilities, testing coverage, and extensive documentation make it an attractive tool not only for scientific and industry users wanting a quick, out-of-the-box solution but also for advanced GNSS users developing new GNSS algorithms. Metadata Metadata for the gnss lib py library is included in the ancillary data table 1. 1. Motivation and significance Global Navigation Satellite Systems (GNSS) are used globally for positioning, navigation, and timing across industries such as transportation, agriculture, power systems, and finance [1]. Several countries and political entities have developed global and regional satellite constellations such as GPS and WAAS (United States), GLONASS (Russia), BeiDou (China), Galileo (the European Union), QZSS (Japan), and IRNSS (India). GNSS technology, policy, and services are an active research area with established research journals and technical conferences.

Deep neural networks for classification are vulnerable to adversarial attacks, where small perturbations to input samples lead to incorrect predictions. This susceptibility, combined with the black-box nature of such networks, limits their adoption in critical applications like autonomous driving. Feature-attribution-based explanation methods provide relevance of input features for model predictions on input samples, thus explaining model decisions. However, we observe that both model predictions and feature attributions for input samples are sensitive to noise. We develop a practical method for this characteristic of model prediction and feature attribution to detect adversarial samples. Our method, PASA, requires the computation of two test statistics using model prediction and feature attribution and can reliably detect adversarial samples using thresholds learned from benign samples. We validate our lightweight approach by evaluating the performance of PASA on varying strengths of FGSM, PGD, BIM, and CW attacks on multiple image and non-image datasets. On average, we outperform state-of-the-art statistical unsupervised adversarial detectors on CIFAR-10 and ImageNet by 14\% and 35\% ROC-AUC scores, respectively. Moreover, our approach demonstrates competitive performance even when an adversary is aware of the defense mechanism.

Language identification is a crucial component in the automated production of language resources, particularly in multilingual and big data contexts. However, commonly used language identifiers struggle to differentiate between similar or closely-related languages. This paper introduces FastSpell, a language identifier that combines fastText (a pre-trained language identifier tool) and Hunspell (a spell checker) with the aim of having a refined second-opinion before deciding which language should be assigned to a text. We provide a description of the FastSpell algorithm along with an explanation on how to use and configure it. To that end, we motivate the need of such a tool and present a benchmark including some popular language identifiers evaluated during the development of FastSpell. We show how FastSpell is useful not only to improve identification of similar languages, but also to identify new ones ignored by other tools.

Uncertainty quantification (UQ) in scientific machine learning (SciML) combines the powerful predictive power of SciML with methods for quantifying the reliability of the learned models. However, two major challenges remain: limited interpretability and expensive training procedures. We provide a new interpretation for UQ problems by establishing a new theoretical connection between some Bayesian inference problems arising in SciML and viscous Hamilton-Jacobi partial differential equations (HJ PDEs). Namely, we show that the posterior mean and covariance can be recovered from the spatial gradient and Hessian of the solution to a viscous HJ PDE. As a first exploration of this connection, we specialize to Bayesian inference problems with linear models, Gaussian likelihoods, and Gaussian priors. In this case, the associated viscous HJ PDEs can be solved using Riccati ODEs, and we develop a new Riccati-based methodology that provides computational advantages when continuously updating the model predictions. Specifically, our Riccati-based approach can efficiently add or remove data points to the training set invariant to the order of the data and continuously tune hyperparameters. Moreover, neither update requires retraining on or access to previously incorporated data. We provide several examples from SciML involving noisy data and \textit{epistemic uncertainty} to illustrate the potential advantages of our approach. In particular, this approach's amenability to data streaming applications demonstrates its potential for real-time inferences, which, in turn, allows for applications in which the predicted uncertainty is used to dynamically alter the learning process.

This week, Emily Bazelon, John Dickerson, and David Plotz discuss the revival of Arizona's 1864 abortion ban; the end of No Labels; and the past and future of presidential debates. Here are some notes and references from this week's show: Mary Jo Pitzl and Reagan Priest for The Arizona Republic: Arizona House GOP halt Democrats' effort to overturn Civil War era law in chaotic session A.O. Sulzberger Jr. for The New York Times: Reagan Says Ban On Abortion May Not Be Needed Thomas B. Edsall for The New York Times: Has No Labels Become a Stalking Horse for Trump? Michael H. Brown for The Washington Post: Joseph Lieberman, senator and vice-presidential nominee, dies at 82 Here are this week's chatters: David: Hannah Seo for The New York Times: Is It Better to Brush Your Teeth Before Breakfast or After? For this week's Slate Plus bonus segment, David, John, and Emily discuss AI communications with loved ones after they die. See Walter Marsh for The Guardian: Laurie Anderson on making an AI chatbot of Lou Reed: 'I'm totally, 100%, sadly addicted' and Ira Glass for This American Life: The Ghost in the Machine.

The U.S. Department of Transportation awarded Southern California's commuter rail system 1.3 million to develop an artificial intelligence-powered security system to detect unexpected movement on Metrolink tracks. The technology would aim to automatically slow down or stop a train when cameras and sensors verified the presence of a person, vehicle or debris, Metrolink said about the proposed "track intrusion detection" system. The technology would integrate with existing GPS that notifies train crew about a possible track danger, such as a homeless encampment or a pedestrian. "If it succeeds, this project will not only improve the safety of our passengers and crew, it will directly benefit pedestrians, cyclists, drivers and everyone else who interacts with our system," Los Angeles City Council President and Metrolink Board member Paul Krekorian said in a statement. The current system, which is also linked to the U.S. earthquake-warning system, relies heavily on what people see and report in real time.

Video captures the moment and aftermath of what appears to be a drone, allegedly of Ukrainian origin, striking Russian drone production facility. Russian officials claimed that only a worker's dormitory was hit. Russia has started to make steady progress against Ukraine as Kyiv's forces face dwindling ammunition supplies, much to Ukrainian President Volodymyr Zelenskyy's frustration. "There can be no question, Ukraine could be quickly overwhelmed by both men and arms by odds as great as 10 to 1 within weeks without additional U.S. assistance," Kenneth Braithwaite, a former ambassador and former Navy secretary during the Trump administration, told Fox News Digital. "This is a critical juncture in the war and time is of the essence for Congress to act on a comprehensive package," Braithwaite said.

Election deniers are mobilizing their supporters and rolling out new tech to disrupt the November election. These groups are already organizing on hyperlocal levels, and learning to monitor polling places, target election officials, and challenge voter rolls. And though their work was once fringe, its become mainstreamed in the Republican Party. Today on WIRED Politics Lab, we focus on what these groups are doing, and what this means for voters and the election workers already facing threats and harassment. Write to us at politicslab@wired.com. Our show is produced by produced by Jake Harper. Jake Lummus is our studio engineer and Amar Lal mixed this episode. Jordan Bell is the Executive Producer of Audio Development and Chris Bannon is Global Head of Audio at Conde Nast. Also be sure to subscribe to the WIRED Politics Lab newsletter here. You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link. Leah Feiger: Welcome to WIRED Politics Lab, a show about how tech is changing politics. Today, we're going to talk about how election deniers are mobilizing their supporters and rolling out new tech to disrupt November.

Forget artificial intelligence breaking free of human control and taking over the world. A far more pressing concern is how today's generative AI tools will transform the labor market. Some experts envisage a world of increased productivity and job satisfaction; others, a landscape of mass unemployment and social upheaval. Someone with a bird's-eye view of the situation is Mary Daly, CEO of the Federal Reserve Bank of San Francisco, part of the national system responsible for setting monetary policy, maintaining a stable financial system, and ensuring maximal employment. Daly, a labor market economist by training, is especially interested in how generative AI might change the labor market picture.