New project to safeguard AI systems used in scientific research
As artificial intelligence becomes central to scientific discovery, researchers face a growing but often overlooked risk: the AI models, datasets, and automated systems they depend on can be compromised in ways that conventional cybersecurity tools are not designed to detect.
A new project called VERITAS (VERified Infrastructure for Trustworthy AI in Science), led by principal investigator Anita Nikolich, research scientist and director of research and technology innovation at the University of Illinois School of Information Sciences, will address this gap by establishing AI Assurance as a core function of scientific research infrastructure. Funded through a three-year, $896,000 grant from the National Science Foundation's Cybersecurity Innovation for Cyberinfrastructure program, VERITAS brings together experts in adversarial AI, research cyberinfrastructure, data science, and workforce development. The project aims to develop practical methods for documenting, reviewing, and stress-testing AI systems before they are used in high-impact scientific workflows.
A blind spot in how science secures AI
Traditional cybersecurity focuses on preventing unauthorized access, catching malware, and stopping data theft. AI-enabled research introduces additional risks that may not trigger conventional security alerts.
A poisoned dataset, for example, may appear statistically normal while causing a model to produce unreliable results. A backdoored model downloaded from a public repository may contain no recognizable malware and may operate normally until a particular input activates its hidden behavior. An autonomous AI agent may have excessive permissions that allow it to alter data, invoke laboratory tools, or manipulate a research workflow.
In each case, the infrastructure may appear secure while the scientific result is compromised.
"We cannot simply bolt traditional cybersecurity onto AI-driven science," said Nikolich. "When a poisoned dataset or backdoored model produces an answer that looks plausible but is subtly wrong, no firewall or virus scanner is likely to catch it. The researchers doing our most important scientific work deserve assurance that the AI systems they rely on are documented, tested, and behaving as intended."
According to Nikolich, rather than requiring scientists to become cybersecurity experts or expecting cybersecurity teams to become machine-learning specialists, VERITAS will integrate AI Assurance into the research infrastructure scientists already use. The project has three connected components:
Model and data documentation. VERITAS will pilot standardized model cards and dataset datasheets for large scientific computing allocations. Similar to nutrition labels on packaged food, these documents describe where a model or dataset came from, how it was created or modified, its intended use, its known limitations, and the assumptions researchers should understand before reusing it. The goal is to improve transparency, reproducibility, and the ability to trace problems through complex AI workflows.
Operational AI security services. VERITAS will pilot a new AI Assurance Engineer role at the National Center for Supercomputing Applications (NCSA). The engineer will review selected technically novel AI projects before deployment, scan model files for unsafe or malicious behavior, examine software for vulnerabilities, and assess the risks around uses of autonomous agents.
Model and data integrity challenges. Through the National Data Platform (NDP) Education Hub, VERITAS will create hands-on challenges that train students to detect poisoned data, inspect potentially compromised models, evaluate agent permissions, and identify weaknesses in scientific AI workflows.
Finding vulnerabilities before they become scientific failures
AI red-teaming—deliberately attacking an AI system to find its weaknesses before an adversary does—is now a well-established field. It has rarely been brought into scientific research, where a manipulated model produces a false result that can pass for legitimate science. VERITAS is among the first efforts to adapt the practice to scientific cyberinfrastructure.
"AI systems can fail in ways that are difficult to distinguish from legitimate scientific results," said Nikolich. "Proactive red teaming allows us to identify those weaknesses before a vulnerable model or agent becomes embedded in a research pipeline. The objective is to help research teams make their systems more trustworthy and resilient."
Building the AI Assurance workforce
VERITAS will also help prepare students for careers at the intersection of machine learning, cybersecurity, and scientific computing. Participants in the project's challenges will work with realistic scientific models, datasets, and infrastructure using NDP while learning about responsible disclosure practices.
By embedding documentation, security review, adversarial assessment, and workforce development into existing scientific cyberinfrastructure, VERITAS seeks to create a model for AI Assurance that can be adopted by supercomputing centers, research institutions, and national-scale AI infrastructure providers.
"AI is now part of the scientific workflow," Nikolich said. "We need to protect its integrity just as seriously as we protect the networks and computing systems around it."
AI model advances scientific discovery with soil carbon research
Cornell University
ITHACA, N.Y. – A new computer model from Cornell University researchers is one of the first artificial intelligence tools to advance scientific discovery in agriculture and biogeochemistry and is 50 times more efficient than its predecessors.
In a paper published in the journal Geoscientific Model Development, the researchers demonstrated the AI on processes behind the important issue of soil organic carbon, as the Earth’s soils hold roughly three-quarters of the world’s terrestrial carbon and more carbon than the atmosphere and all the world’s plants combined.
Scientists have been exploring ways to use AI for research purposes, but most common AI tools, such as ChatGPT, mainly repurpose existing information. Researchers have also used AI to extract patterns from data. But the new model, called the Biogeochemistry-Informed Neural Network (BINN) goes a step further by predicting biological processes that are not yet well understood and suggesting factors that control them.
“BINN is very easy to use and can be democratized among the scientific community in various disciplines,” said Yiqi Luo, the senior author of the study. “This is one of the first tools of this type that can promote scientific research with AI.”
Soil scientists know the mechanisms by which soils acquire organic carbon – plants extract and sequester carbon from carbon dioxide to grow, and when those plants die, organic matter from stems, leaves and roots decompose into smaller and smaller bits to become part of the earth. But what is not well known are the speed of these processes and how many such processes are required to break down the litter.
“We use AI and data to tell us quantitatively how fast and how many of these kinds of processes are required,” said Haodi Xu, a doctoral student in Luo’s lab, and co-first author of the study.
When compared to previous models, BINN computed 50 times faster. The accuracy of predictions of quantities of soil organic carbon was found to be very similar to the previous models. But previous models contained spatial biases, meaning that when making predictions across the contiguous U.S., it might favor the data from one area versus another. The researchers found less spatial bias with BINN.
For additional information, read this Cornell Chronicle story.
Cornell University has dedicated television and audio studios available for media interviews.
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Journal
Geoscientific Model Development
AI underwater robots can now track diver stress via exhaled bubbles
University of Minnesota breakthrough marks the first time robotic vision has been used to monitor human respiration rate underwater
image:
During an open-water experiment in the Caribbean Sea off the coast of Barbados, the researcher team tested the autonomous underwater vehicles (AUVs).
view moreCredit: Photo provided by Junaed Sattar
MINNEAPOLIS / ST. PAUL (07/27/2026) - University of Minnesota Twin Cities researchers have developed a first-of-its-kind AI system that allows underwater companion robots to monitor a diver’s health in real-time, simply by "watching" their exhaled bubbles.
Published in The International Journal of Robotics Research, the paper marks the first time robotic vision has been used to estimate a diver’s Human Respiration Rate (HRR).
Scuba diving, particularly in extreme environments, is inherently risky and places humans under intense physical stress — ranging from exhaustion to life-threatening respiratory distress. By tracking the frequency and volume of bubbles exhaled from a diver’s regulator, camera-equipped robots can now detect signs of stress, hyperventilation or exhaustion in real-time.
This non-contact approach solves a long-standing challenge where traditional medical sensors and wearables often fail underwater because thick wetsuits or drysuits block the contact needed for accurate readings. Wireless data transmission through water is also severely limited.
“Our goal was to give divers a dedicated robotic safety partner to provide a second set of ‘eyes’ capable of reading physiological stress underwater,” said Junaed Sattar, Associate Professor in the Department of Computer Science and Engineering and senior author on the paper. “This work is a first step towards assessing not just one but a group of divers in the robot's field of view.”
To train the AI model, the research team developed a "fuzzy labeling" system. Because underwater footage can be murky, they manually categorized thousands of images while using synchronized audio cues made up of the distinct sound of regulator exhalations to teach the robot exactly what a breath looks like.
“While monitoring breathing is a standard vital sign on land, doing so underwater presents immense technical challenges,” said Demetrious Kutzke, a Ph.D. student in the Robotics & Vision Laboratory at the University of Minnesota and the study’s lead author.
To meet these challenges, the team compiled an extensive dataset of audio and visual recordings from various environments to ensure the AUV could operate in different water temperatures and levels of clarity. Data collection spanned locations from Lake Superior in Duluth, Minn. and Square Lake in Stillwater, Minn., to the Caribbean Sea off the coast of Barbados.
At the core of the field trials was a communication system called HREyes, where the robot could notify its human dive partner of their status, categorizing their breathing as "below-normal" (<14 breaths/min), "normal" (14–20 breaths/min) or "above-normal" (>20 breaths/min). By converting these visual observations into breaths-per-minute, the robot can determine if a diver is under duress.
Looking ahead, the team plans to pair breathing-rate data with the analysis of diver movement. Merging these metrics will provide a comprehensive “wellness profile" to ensure maximum safety during deep-sea explorations.
In addition to Sattar and Kutzke, the research team included Vennela Dupati, undergraduate student in the Department of Computer Science and Engineering and the Department of Electrical and Computer Engineering.
This research was supported in part by the Science, Mathematics, and Research for Transformation (SMART) Scholarship from the U.S. Department of Defense and the National Science Foundation.
Read the full paper entitled, “Robotic estimation of single scuba diver respiration rate for safety in underwater human-robot collaboration,” on the Sage Journal’s website.
Aerial perspective of the field evaluation setup.
Credit
Junaed Sattar
Journal
The International Journal of Robotics Research
Article Title
Robotic estimation of single scuba diver respiration rate for safety in underwater human-robot collaboration
Identify birds offline using AI: New app records animal sounds directly on your smartphone
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Martina Buchna, a ranger at the Bavarian Forest National Park, and developer Dr Stefan Kahl are convinced of the strengths of the BirdNET Live bird-song app. The ranger demonstrates an audio logger that can be left hanging in the forest for several days to record sounds. Training data for the app was collected using this device.
view moreCredit: Gregor Wolf / Bavarian Forest National Park
### Joint news release from Julius-Maximilians-Universität Würzburg (JMU), University of Technology Chemnitz, and the Bavarian Forest National Park Authority ###
Previous apps for identifying animal calls regularly reached their limits in practice: without a stable internet connection, real-time identification in the forest or high mountains was impossible.
The new BirdNET Live app, a further development of the BirdNET app, solves this problem with a technological breakthrough: the underlying AI models have been optimised, streamlined and accelerated to such an extent that they can now run directly on the smartphone.
Around 11,000 bird species are currently known worldwide; the app can currently recognise 8,927 of them. It is also capable of identifying the calls of 268 mammals, 254 insects and 340 amphibians, in real time and completely offline. In total, BirdNET Live can identify almost 10,000 animal species by their sounds – more than any other app, as lead developer Dr Stefan Kahl from the Professorship of Media Informatics at the University of Technology Chemnitz explains: the next-best offline app can only identify around 2,300 species.
As an open-source project, BirdNET Live offers maximum usability. The app’s user interface is currently available in seven languages; the names of the identified animal species are translated into 25 languages.
A tool for experts and the general public
Whilst conventional identification apps are primarily intended for occasional use in one’s own garden, BirdNET Live is aimed first and foremost at nature conservation professionals, researchers, students and ambitious citizen scientists. Amateur ornithologists can, of course, also use it.
The app features specialised modes for structured surveys, as are standard in scientific fieldwork. It automatically records animal calls with precise location and time data and stores them locally on the smartphone.
The recordings can subsequently be flexibly reviewed, exported or shared with other experts for validation. At the same time, the app serves as a learning tool: users can listen back to their own acoustic recordings directly and thus continuously improve their species identification skills.
Deutsche Bundesstiftung Umwelt funded the development team
BirdNET Live is the result of close interdisciplinary collaboration as part of the RangerSound project, which was funded by the Deutsche Bundesstiftung Umwelt (DBU).
To identify the shortcomings of existing bioacoustic systems in everyday practical use, conservation biologists from the University of Würzburg, the rangers at the Bavarian Forest National Park, hardware experts from the company Oekofor (Freiburg / Breisgau) and AI specialists from University of Technology Chemnitz worked hand in hand.
The result is a practical and robust app designed specifically for field conditions. When used with an external microphone, for example, the smartphone can be carried safely and waterproofed in a rucksack whilst the app continuously records data in the background.
BirdNET Live is an enormous technological progress
“At first glance, running the AI locally on a smartphone seems like a small, logical step forward. However, behind this lies enormous technological progress in optimising our AI models,” says Dr Stefan Kahl from University of Technology Chemnitz, an AI expert and lead developer of BirdNET.
The app demonstrates just how important interdisciplinary collaboration is: “It was only when practitioners, conservation biologists and we, as AI developers, sat down together that we were able to identify the real shortcomings in the field and create a tailor-made, practical solution. This is a model that can be applied to many areas where AI is finding its way into our everyday lives.”
Professor Jörg Müller: “An app for students too”
“We will now be using BirdNET Live as standard in our university teaching. This will give our students entirely new opportunities to collect standardised data for their dissertations or during field courses,” says Professor Jörg Müller, Chair of Conservation Biology and Forest Ecology at the University of Würzburg.
The app’s potential extends far beyond local forests: “We expect the app to serve us exceptionally well in our research work in the extremely species-rich lowland rainforest in Ecuador, where there is no mobile phone coverage whatsoever.”
Dr Simon Verdon: “Ideal for rangers”
“Local analysis carried out directly on site is exactly what we’ve been hoping for in the day-to-day work of ranger teams in protected areas,” says Dr Simon Verdon from the University of Würzburg, a member of the RangerSound project: “They can simply leave the app running during their patrols and immediately get a clear picture of the local species composition without having to rely on mobile network coverage.”
Further facts about BirdNET
• De facto standard: BirdNET is an AI system for the automatic recognition of bird calls and the world’s leading technology for AI-supported bioacoustic monitoring.
• Successful citizen science project: With more than two million active users worldwide and over six million app downloads, more than 250 million verified nature observations have already been collected.
• Important for science: The data collected from citizen science projects feeds directly into global research projects. For example, BirdNET data collected via the BirdWeather network provided the basis for a study published in the renowned journal Nature on the impact of light pollution on birds’ singing behaviour.
• Partners: BirdNET is a joint project between University of Technology Chemnitz and the Cornell K. Lisa Yang Center for Conservation Bioacoustics (USA).
The ‘Schlaumeise’ portal helps you get started
For aspiring citizen scientists, schools, teachers and learners, the schlaumeise.org portal offers supporting materials, tutorials and project ideas covering the topics of bioacoustics, species conservation and artificial intelligence.
Further information on the app and how to download it:
• Official project website: birdnet.tu-chemnitz.de/live-app
• Portal for learners and teachers: www.schlaumeise.org
Here, chaffinches, robins and blue tits are singing: with the BirdNET Live app, even non-experts can identify bird songs in real time.
In transect mode, the BirdNET Live bird song app runs for several hours. This is useful for applications in national parks, ecology and nature conservation, as it allows bird songs to be assigned to specific points along a route that has been walked.
Credit
Stefan Kahl / University of Technology Chemnitz
