TECH

Artificial intelligence has accelerated system protection but created a bottleneck

For decades, discovering a serious software vulnerability required weeks of investigation, highly specialized professionals, and often a good deal of luck. This landscape has changed radically with the advancement of artificial intelligence. Today, automated systems can analyze millions of lines of code in just a few days and identify issues that would have previously gone unnoticed. However, this evolution has brought an unexpected consequence that is beginning to worry cybersecurity experts worldwide.

The digital security sector is undergoing an unprecedented transformation. AI-based tools are identifying vulnerabilities at such a high speed that the teams responsible for remediation are struggling to keep pace with this new tempo.

The latest figures clearly illustrate this shift. Major technology companies have recorded significant growth in the number of flaws patched in their flagship products, indicating that the capacity to detect issues has evolved much faster than traditional software update processes.

One of the most notable examples involves Microsoft. After recording a record number of patches in June 2026, the company surprised the industry again just a month later by releasing an even larger update; this included hundreds of vulnerability fixes—dozens of which were classified as critical—and addressed several flaws that had been exploited before the updates were even made available.

This trend was not limited to a single company. Widely used web browsers and enterprise platforms also saw historic increases in the number of vulnerabilities identified in their codebases.

While automated analysis tools have existed for years, the recent surge occurred because AI models have moved beyond simply testing random data combinations. They can now understand how the code functions, establish relationships between different parts of the system, and predict how minor flaws might be combined to launch more sophisticated attacks.

In practice, tasks that once relied exclusively on highly experienced researchers are now being performed at scale by AI models specifically trained for this purpose. The volume of discoveries is impressive, yet it creates a new bottleneck for the entire industry... The impact of this advancement has been evident in several major projects. The Google Chrome browser, for instance, saw the number of vulnerabilities patched in just two versions exceed the total identified over the preceding two years.

According to the company, artificial intelligence played a decisive role in this surge, significantly accelerating the flaw-identification process. Consequently, Chrome began testing an even faster pace for rolling out security updates to reduce the time between discovering a problem and fixing it.

A similar situation unfolded with Firefox. Following successful experiments using models developed by Anthropic to locate significant vulnerabilities, Mozilla expanded its use of these tools and uncovered hundreds of new issues in a single browser version.

Companies like Oracle also recorded a substantial increase in the number of patches released for their products. Most of these vulnerabilities were identified through internal analysis, reinforcing the trend of using artificial intelligence as a key ally for security teams.

However, there is an important detail that prevents alarmist interpretations: not all vulnerabilities pose the same level of risk. Amidst thousands of published patches, only a relatively small fraction represents truly critical threats to companies and users.

Even so, this avalanche of discoveries creates a new operational challenge.

Finding the flaws is no longer the problem; the challenge now is deciding what to fix first... As artificial intelligence exponentially increases the number of discovered vulnerabilities, system administrators face an increasingly complex task: setting priorities.

Each update requires careful analysis before installation. In corporate environments, applying hundreds of patches without a plan can lead to incompatibilities, critical system outages, and operational impacts just as significant as the flaws they aim to fix.

Consequently, security teams must assess which vulnerabilities pose the highest risk of immediate exploitation, which can wait for a scheduled maintenance window, and which have a negligible impact on a specific infrastructure.

This process demands technical expertise, testing, and planning—activities that still rely on human experience, even with the growing role of artificial intelligence.

The current landscape reveals a significant shift in the cybersecurity sector. For years, the primary challenge was uncovering vulnerabilities hidden within millions of lines of code. Technology has now largely resolved that issue.

The new hurdle lies in managing an ever-expanding volume of information. While the risk used to be overlooking flaws, the challenge today is transforming thousands of findings into effective patches without compromising system stability.

Paradoxically, the more efficient artificial intelligence becomes at hunting for vulnerabilities, the greater the responsibility placed on human teams to decide how, when, and which issues truly need to be addressed first.

mundophone

 

TECH

Harnessing statistics to improve personalized medicine

The future of federally funded research at Harvard Medical School — supported by taxpayers and done in service to humanity — remains uncertain. Learn more.

Scientific advancements — including understanding health conditions and developing new treatments — hinge on data and its implications. Statistician Alex Luedtke is developing new methods to make data analysis as efficient and effective as possible with the goal of improving personalized treatment recommendations in mental health and other fields.

Luedtke joined the faculty in the Blavatnik Institute at Harvard Medical School last summer as professor of health care policy. His work combines statistical methodology with machine-learning techniques to draw cause-and-effect conclusions from messy biomedical data and speed discovery.

“I really enjoyed the mathematical sciences, but I wanted to do something with them that felt meaningful,” said Luedtke, who previously held faculty appointments at the University of Washington and Fred Hutch.

Luedtke has received an Emerging Leader Award from the Committee of Presidents of Statistical Societies, the Mortimer Spiegelman Award, and a National Institutes of Health Director’s New Innovator Award.

Harvard Medicine News spoke with Luedtke about his collaborations at Harvard and his ongoing efforts to build new approaches to data analysis that could facilitate personalized medicine and improve patient outcomes.

Harvard Medicine News: Tell us a little bit about the focus of your research.

Alex Luedtke: I’m a statistician. Causal inference and machine learning are my two main areas, so broadly speaking, I try to pull cause-and-effect answers out of real-world data.

A lot of my work focuses on what we call efficiency theory, which is a fancy name for using data as well as possible. We’re trying to get the most precise answers possible from a limited amount of data.

One specific question I’m working on, which I started back during my doctoral dissertation at the University of California, Berkeley, is the extent to which individualizing treatment decisions can help improve outcomes for people. Based on patient characteristics, how can we best decide whether we should give them treatment A, B, or C? In my previous role, I did this in infectious disease settings, but increasingly nowadays I’m doing it in mental health with collaborators here at Harvard.

HMNews: What infectious diseases were you working on?

Luedtke: I worked on vaccines trials, both for HIV and then also COVID. I served as a study statistician, which meant that I would monitor the trials as they went and also determine what statistical methods would be used to analyze the data from those trials.

Clinical trials are a classic use case for efficient statistical methods. It’s very expensive to recruit new people into a trial and to run a trial longer to keep accruing more information about participants. And ethically, you want to be able to terminate a trial if a vaccine isn’t working. So it’s best for everyone involved to figure out whether a vaccine is effective as quickly as possible.

HMNews: What do your current mental health collaborations look like?

Luedtke: I actually started collaborating with [McNeil Family Professor of Health Care Policy] Ron Kessler, who is in my current department, back in 2016. In our collaborations, we work a lot on figuring out whether individualization will help for treating mental health disorders. We’ve looked at depression and at schizophrenia. We take the statistical methods that I develop for treatment individualization and apply them directly to his messy datasets, most of which are from observational, non-randomized trials.

That’s been a lot of fun. When statisticians develop new statistical methods, we always develop them in the cleanest possible setting where we get to see the outcomes on everyone, and we have a random sample from the population we care about. The moment we start touching real data, we don’t get that. Patients drop out of the study or are just lost in the registry. And sometimes the patients who drop out are different than other patients in some meaningful way that actually has something to do with whether the treatment works.

And so a lot of my work with Ron is looking at each particular dataset and asking: How was the data generated? Why are data missing? What do we have to do statistically to make sure that we can develop unbiased conclusions at the end? Ultimately, that analysis helps us determine if it’s worth individualizing treatments for these mental health disorders and make recommendations to help physicians provide the best care for their patients.

HMNews: So how do machine learning and AI come into it?

Luedtke: AI looks at prediction problems — trying to predict what will happen in the world we have now. Causal inference asks: If we were to go in and change something in the world — introduce a new treatment or make a treatment more widely available — what will happen?

The challenge is that we’re changing something. Even if we have data from the world as it is now, we won’t have data from the world as we want it to be. One of the projects I’ve looked at is training these generative AI tools to essentially create new data for a world in which we change the treatment.

It’s similar to how we train existing generative AI models, but with the extra challenge that we want to determine cause and effect and we don’t have existing data from that scenario.

HMNews: I know you’re just getting started here, but over the coming years — or decades — what are you hoping to accomplish? Where do you want to move the needle forward in your field?

Luedtke: Historically, statisticians derive new methods by sitting down with a pen and paper and writing out how we think the data were generated and then spending a bunch of time building an estimator — a new way to analyze that data. Then methodological statisticians work to prove those results, essentially certifying the analysis strategy. Once that’s done, we can go out and use it on real data. This is a lot of what statistics has been for a hundred years.

I’m very interested in speeding up this process. If you tell me that I’m going to get a new dataset that was generated in a certain way with specific quirks, I’d rather avoid sitting down with the pen and paper at all.

This isn’t a new interest for me. A few years ago, we published a paper in Science Advances on getting a machine to work out a statistical procedure on its own, rather than a person deriving it by hand. Back then we had to train a model from scratch for every new statistical setting we wanted to handle.

Now AI models can carry what they learn in one setting into settings they’ve never seen. This ability is behind the scientific foundation models people are building in other fields, where a single model generates predictions across a whole scientific domain. I’m trying to build something similar for statistics: give the model a new statistical setting, and it works out the estimator. We have to be very careful about how we build it, but it could let us skip the slowest and most error-prone parts of the current process.

If we can do that, we can speed up how quickly we are able to draw scientific conclusions from data and translate them into treatments that can start helping patients.

 by: LAURA CASTAÑÓN---https://hms.harvard.edu/news/search?topic%5B21%5D=21

TECH

Can wind capture atmospheric water and convert it into freshwater?

A new atmospheric water harvesting system could turn wind directly into drinking water, without first converting the wind’s energy into electricity. The technology combines highly porous hygroscopic polymer sponges with eddy current heating, creating a compact approach designed to extract moisture from air and release it as freshwater. In experiments conducted under changing outdoor conditions, the system produced as much as 9.9 liters of water per day for every kilogram of sorbent material.

The work, published in Advanced Functional Materials, addresses a major limitation of many sorption-based atmospheric water harvesting systems. These systems use materials that attract and retain water vapor from humid air, but the captured water must later be removed through a regeneration step. Conventional designs generally rely on sunlight or electrically powered heaters to warm the sorbent. That additional energy requirement can restrict their use in remote locations, especially where electrical infrastructure is unreliable or absent.

The new strategy relies on hygroscopic polymer sponges engineered with a highly interconnected macroporous structure. Their open network of pores provides a large internal surface area and allows humid air to move efficiently through the material. Hygroscopic chemical groups within the polymer attract water molecules from the atmosphere, causing the sponge to absorb moisture even when the surrounding air is not saturated. Once loaded with water, the sponge can be heated so that the absorbed moisture evaporates and can be collected as liquid freshwater.

The researchers integrated the sponges with a wind-driven eddy current heating system. Eddy currents are circulating electrical currents induced inside a conductive material when it is exposed to a changing magnetic field. The electrical resistance of the material converts these currents into heat. In the reported device, wind energy powers the mechanical process that generates the changing magnetic field, allowing the system to produce heat directly rather than sending the energy through a wind turbine, electrical generator, and separate heater.

That direct energy pathway is central to the system’s claimed efficiency. According to the researchers, the wind-powered heating process achieved an energy conversion efficiency exceeding 90 percent. By avoiding intermediate electricity generation, transmission, and electrical heating stages, the design can reduce energy losses and simplify the hardware required for sorbent regeneration. The approach also allows the heating process to operate independently of sunlight, potentially extending water production into cloudy weather, nighttime operation, and locations where solar energy is inconsistent.

During operation, the polymer sponge first captures water vapor from ambient air. When the sponge reaches its moisture capacity, wind activates the eddy current heating component, raising the temperature of the sorbent. The heat weakens the interactions between the hygroscopic polymer and the captured water, driving evaporation. The released vapor is then directed toward a cooler surface, where it condenses and can be collected. Repeating the adsorption and desorption cycle allows the same sponge material to harvest water continuously.

The reported production rate—9.9 liters per day per kilogram of sponge—was measured under fluctuating ambient air conditions rather than in a perfectly controlled, constant-humidity environment. That detail is important because atmospheric water harvesting performance depends strongly on relative humidity, temperature, wind speed, and the duration of each adsorption and regeneration cycle. A sorbent may collect water rapidly during humid periods but require longer exposure when the air is dry. The system’s performance will therefore vary from one climate and season to another.

The researchers believe the technology could be especially valuable in wind-rich coastal and island communities, where atmospheric moisture and wind resources are abundant but freshwater supplies and electrical grids may be limited. It could also serve remote settlements, emergency response operations, and off-grid facilities that need a decentralized source of water. Unlike systems dependent on large solar collectors or grid-connected heaters, a wind-driven design could be deployed in areas where strong winds are available throughout much of the day.

The system is not intended to make freshwater production independent of engineering constraints. Practical deployment will require durable sorbents that can withstand repeated swelling, drying, heating, and cooling cycles. The device must also manage airborne dust, salt, and other contaminants, particularly in coastal environments. Water quality will depend on the composition of the sorbent, the collection surfaces, and any purification steps added after condensation. Further testing across dry, humid, hot, and cold climates will be needed to determine how consistently the laboratory-scale performance can be maintained in long-term operation.

Even with those challenges, the combination of atmospheric moisture capture and direct wind-to-heat conversion offers a new direction for renewable water technology. Instead of treating wind solely as a source of electricity, the approach uses it as a direct thermal resource for regenerating a moisture-filled sorbent. If the materials remain stable and the system can be scaled economically, wind-driven atmospheric water harvesting could provide a flexible source of freshwater for communities facing water scarcity and limited access to conventional infrastructure.

References: Li, Haiqing et al., “Wind-Driven Atmospheric Water Harvesting Enabled by Highly Interconnected Macroporous Hygroscopic Polymer Sponges and Eddy Current Heating”

SONY

Sony makes 100 – 400 mm full-frame zoom lens 80% cheaper and 65% lighter

Sony demonstrates that full-frame zoom lenses with long focal lengths don’t have to be heavy or expensive. While the brand-new FE 100–400 mm f/5.6–8 isn’t particularly fast, it is comparatively compact and affordable. Sony nevertheless promises fast autofocus, optical image stabilization, and sharp photos.

Sony is expanding its lens lineup with a third 100–400 mm zoom lens. However, while the two older models were relatively fast for a telephoto zoom lens with a maximum aperture of f/4.5, the new model has a maximum aperture ranging from f/5.6 to f/8, depending on the focal length. This also makes it more difficult to use teleconverters, which effectively reduce the lens’s light-gathering ability even further.

Super-telephoto shooting in a compact, lightweight package...This lightweight, compact 100–400mm super-telephoto zoom lens weighs just 654 g (23.1 oz.), with a maximum diameter of 78.2 mm (3-1/8 in.) and a length of 164.5 mm (6-1/2 in.). Fill the frame with distant subjects and use telephoto compression to achieve unique perspectives in panoramic shots. An optional teleconverter extends the reach to 800 mm—or 1,200 mm in APS-C format.

This decision offers significant advantages in terms of the lens’s price and weight. With a list price of $850, the new lens is $3,450, or 80 percent, cheaper than the Sony FE 100–400 mm f/4.5 GM. Weighing 654 grams, the new lens is also about 1.2 kilograms, or 65 percent, lighter. The lens is 16.4 cm long with a diameter of 7.8 cm. Despite its lower price, Sony has opted for a complex design consisting of 15 elements in 10 groups and includes Optical SteadyShot (OSS).

An optical design featuring two ED glass elements and two aspherical elements effectively reduces chromatic aberration and other distortions, delivering sharp, clear subject rendering at both close and long ranges. A 9-blade circular aperture contributes to smooth, natural bokeh. With up to 0.41x magnification and a minimum focusing distance of just 0.86 m, you can also capture impressive close-ups of flowers and insects.

Easily capture challenging subjects handheld...Dual linear motors drive fast, precise, and quiet autofocus, effortlessly locking onto and tracking moving subjects. When paired with the α9 III body, continuous shooting at up to 120 fps is possible with full autofocus performance. The lens's optical stabilization also works with compatible camera bodies to deliver enhanced stabilization performance, effectively compensating for the pronounced blur that can occur with telephoto lenses and ensuring consistently stable framing.

Two ED lenses and two lenses with aspherically ground surfaces are designed to deliver high image quality across the entire zoom range. With a minimum focus distance of 0.86 meters, the lens achieves a maximum magnification of 0.41. The lens barrel features three sliders that can be used to lock the zoom, switch between autofocus and manual focus, and turn image stabilization on and off.

Pricing and availability...The Sony FE 100–400 mm f/5.6–8 OSS full-frame zoom lens will be released in late August 2026 at a suggested retail price of $850.

 

mundophone

TECH

"AI can optimize, but it doesn't replace the information captured by the lens," says Zeiss executive

German company Zeiss, one of the world's leading manufacturers of lenses and optical technologies, has been adapting to a landscape of photography increasingly shaped by artificial intelligence. Today, in partnership with the Chinese company vivo Mobile—which owns the Jovi brand in Brazil—Zeiss is launching a camera system designed for the X300 smartphone series, comprising the Ultra and Fashion Edition models.

In an exclusive interview with *O Globo*, Oliver Schindelbeck, Senior Manager of Smartphone Technology at Zeiss, states that lens and smartphone manufacturers will increasingly need to work together to develop more powerful cameras. "The next major steps likely won't come from a single 'magic' component, but from fine-tuning all the elements," he said.

How has the company been adapting traditional camera lenses for smartphones that are becoming increasingly thin and AI-equipped?

Today, imaging systems need to be intelligently designed, incorporating advances in multi-camera setups, chips, software, and AI. Yet, the fundamental laws of optics remain the same, whether working with a professional camera lens or within the space of a few millimeters inside a smartphone. Light still needs to be guided, directed, and corrected. In a smartphone, however, users still expect a device that is thin, lightweight, and convenient. This means every element requires precise balancing: lens design, materials, coatings, mechanical tolerances, sensor characteristics, manufacturing capabilities, and software processing.

And how is AI changing cameras?

AI is a very powerful tool in mobile imaging, but that doesn't make optics any less important. In fact, I would say it increases the need for a solid optical foundation. Anything the lens fails to capture correctly is either lost or must be reconstructed later—and reconstruction always has its limits. AI can assist with scene understanding, segmentation, noise reduction, depth mapping, or stabilization, but the foundation remains the optical image that reaches the sensor. The best results come from when optics and AI complement each other.

Is there a limit to what AI can achieve?

Yes, there are limits. AI can optimize, interpret, and enhance the image, but it cannot replace the physical information captured by the lens. A good lens helps deliver sharp detail, contrast, natural rendering, and stray light control before image processing even begins. Especially in challenging situations—such as strong backlighting, night scenes, telephoto shots, or fine textures—optical quality remains decisive. In other words, software can do a lot, but it works best when starting with high-quality optical data. The greatest progress occurs when both worlds develop in close alignment.

And how does the joint development process with a smartphone manufacturer work in practice?

It is a long-term collaborative engineering process. We have a six-year partnership with Jovi. We contribute expertise in optical design, coatings, validation, color, and image standards. Meanwhile, a manufacturer like Jovi leads the integration with the smartphone, software, and AI, as well as product implementation.

How do you adapt optical knowledge for an era where algorithms actively shape the final image?

In traditional photography, we think primarily about the lens, sensor, and post-processing. In smartphones, the image is shaped by an even closer interaction between optics, the sensor, the chip, software, AI, the display, and the user interface. Our role is to help translate optical principles and image standards into this compact, computational environment. A good example is portrait rendering. The goal isn't just to blur the background, but to create a believable visual impression that reflects the character of photographic optics.

Today, a smartphone packs a main lens, an ultra-wide, a telephoto lens, and various zoom levels into a space of just a few millimeters. What is the biggest engineering challenge in delivering this range of focal lengths?

The biggest challenge is physical space. Size matters—especially for smartphones—because consumers prefer devices that are slim and easy to carry. At the same time, focal length literally implies length. We need space to guide light through the lens system, particularly for telephoto imaging. But these limitations don't mean innovation stops; on the contrary, they make collaboration even more crucial. Progress stems from a combination of superior optical designs, new materials, more effective coatings, smarter stabilization, more powerful chips, AI-driven processing, and precision manufacturing. The next major leaps likely won't come from a single "magic" component, but from fine-tuning every element of the system so they mutually enhance one another's performance.

Can the smartphone already be considered a professional tool for photographers?

Professional cameras remain vital for specific workflows, specialized lenses, larger sensors, controlled production environments, and maximum flexibility. Smartphones aren't simply replacing professional cameras; they complement them and make professional-quality imaging accessible to a much wider range of users.

How does the company view the future of smartphone lenses? Will we continue to see multi-camera devices, or is the trend shifting toward simplifying hardware and offloading more of the workload to AI?

I expect both trends to continue. Multiple cameras and varied focal lengths are useful because they offer users different perspectives. Meanwhile, AI and computational photography will make transitions between these perspectives smoother and improve the final result. I don't believe the future is simply about less hardware and more AI. The future lies in a more integrated system that operates so naturally that the user can focus on the subject rather than the technology behind it.

mundophone

 

TECH

SBC35-474: Industrial single-board computer with Windows support, HDMI, DisplayPort, and M.2

A new single-board computer is designed for demanding environments and supports both Windows and DDR5 memory. The SBC35-474 also offers high-speed network connectivity and accommodates multiple M.2 expansion cards.

The manufacturer Winsystems has added a new single-board computer to its lineup: the SBC35-474. It is described as a “rugged SBC,” a term that may need some clarification. The board is not as well protected as, for example, a waterproof and drop-resistant rugged smartphone; instead, “rugged” refers to its suitability for industrial applications. The system can operate at temperatures ranging from –40 °C to 85 °C, making it suitable for use in uncooled outdoor enclosures in both summer and winter. The 146 x 102 mm board is not really comparable to a Raspberry Pi—not only in terms of size but also in its target audience—and the same applies to the processors. Depending on the configuration, it is equipped with an Intel Atom x7211RE, x7433RE, or x7835RE. The top model offers eight processing cores and has a TDP of 12 watts, so the use of a heat sink is generally recommended.

Supported operating systems include Ubuntu and Windows 11 IoT. The system features 8 or 16 GB of DDR5 memory, along with 128 GB of eMMC storage. Additional storage is supported. A total of three M.2 slots are available, with the M.2 2280 slot supporting SSDs via SATA or NVMe. The M.2 2230 slot is intended for a wireless module, while the third M.2 slot can be used to add cellular connectivity via a SIM card. Network connectivity is provided via two Ethernet ports, each supporting data rates of up to 2.5 Gbit/s. The board can drive up to three displays simultaneously via HDMI 2.0, DisplayPort, and eDP, with the eDP connection exposed through a 40-pin connector.

There are 10 GPIO pins for connecting external sensors and actuators, as well as two serial ports. This allows the SBC35-474 to be connected, for example, to industrial machinery, with status data transmitted via Ethernet to a control system. USB 3.0 and USB 2.0 are also supported, making it feasible to use the board on-site with a keyboard, mouse, and monitor. As is common for B2B products, pricing information has not been publicly disclosed. 

Processor options include Intel Atom x7211RE, x7433RE, and x7835RE with scaling from dual-core to eight-core performance hosting processor frequencies up to 3.6 GHz and integrated Intel UHD Graphics featuring 16 execution units.

The fanless platform has an operating temperature range of -40°C to +85°C and includes three M.2 expansion sockets for wireless connectivity, storage, AI accelerators, and other application-specific peripherals.

It provides local processing, AI acceleration, high-speed networking, and hardware security by utilizing the following features:

DDR5 memory support

Dual 2.5 GbE ports

TPM 2.0 hardware security

Triple-display support

Windows 11 IoT and Ubuntu Linux support

“Organizations developing industrial and infrastructure systems need more than processing performance. They need a platform they can standardize on for years,” said Robert Dunaway, Chief Revenue Officer, WINSYSTEMS. “The SBC35-474 delivers the rugged design, Intel performance, connectivity, long life-cycle support, and US-based manufacturing to help engineering teams innovate and accelerate deployment while helping project managers minimize program risk.”

The SBC35-474 is designed for industrial automation, machine control, machine vision, transportation systems, communications gateways, remote monitoring, and smart infrastructure applications.

The SBC35-474 is designed for organizations deploying mission-critical systems in harsh environments. Typical deployments include industrial automation and process control, machine vision, test and measurement equipment, transportation systems, communications infrastructure, energy management, security systems, remote monitoring, and other intelligent edge applications.

Whether deployed in factory automation, transportation, energy, or communications infrastructure, edge systems increasingly require a combination of local intelligence, rugged reliability, and long-term availability. The SBC35-474 is designed to provide that foundation across a wide range of industrial computing applications.

“Organizations developing industrial and infrastructure systems need more than processing performance. They need a platform they can standardize on for years,” said Robert Dunaway, Chief Revenue Officer, WINSYSTEMS. “The SBC35-474 delivers the rugged design, Intel performance, connectivity, long life-cycle support, and US-based manufacturing to help engineering teams innovate and accelerate deployment while helping project managers minimize program risk.”

mundophone

TECH

When the fake boss calls: a real-time warning system for fake videoconferences

Videoconferencing facilitates communication between different locations while still allowing people to look each other in the eye. However, more and more often we have to question whether the other person's eyes are real. This is because deepfake technologies are now capable of falsifying voices and images in real time more realistically than ever. In 2025, the CFO of a company in Singapore was ensnared by a deepfake Zoom call in which all the participants were AI-generated—including his boss. In the end, he got off lightly: The authorities were later able to recover the roughly 500,000 U.S. dollars he had transferred.

The Cyber Security Agency (CSA) in Singapore alone has recorded a massive increase in video fraud cases like this in the first quarter of 2026. The country saw over 1,200 cases in January alone. That number was 43 in the same month of the previous year. However, the problem is global. After analyzing over one billion cases worldwide, the 2026 Entrust Identity Fraud Report concludes: “Identity fraud is no longer a crime of opportunity. It has now become industrialized, globally organized and commercially optimized.” This is cause enough for Fraunhofer researchers such as Martin Steinebach to work intensively on this issue. Steinebach heads the Media Security and IT Forensics department at the Fraunhofer Institute for Secure Information Technology SIT in Darmstadt.

“Videoconferences represent a special challenge for deepfake detection,” says the expert. “Image and sound quality fluctuate with variations in the network. Because the data streams are compressed, those specific image artifacts often used by earlier detection methods to identify deepfakes are lost.” The challenge is further exacerbated by automatic blur filters, variations in lighting as well as noise and movement in the background. A detector cannot be allowed to produce false positives for these normal videoconferencing effects.

A local solution for real-time detection...In an ATHENE research project, Steinebach and his team of experts at Fraunhofer SIT and at the Fraunhofer Heilbronn Research and Innovation Center (HNFIZ) for Cybersecurity have developed a solution combining video and audio deepfake detection. The AI-based software is designed to continuously inform participants in security-critical video calls of the likelihood of a deepfake. The warning is visual: “If a meeting is currently raising many flags, the system indicates a probability that it is a deepfake,” the researcher says. “But then the person in the meeting still has to verify the other party's authenticity by asking specific questions or by calling back on a different channel.”

The analysis can run locally on a high-performance laptop without requiring the transfer of image or audio data to an external server. It is thus compliant with data protection requirements and is suitable for companies conducting confidential meetings, such as with executive boards, finance departments or external partners. “A computer with a modern graphics card and twelve gigabytes of graphics memory is sufficient for the local real-time analysis in our solution,” says Steinebach.

This solution should also prove interesting to videoconferencing systems providers who could integrate it as a plugin or as an embedded security feature in systems such as Teams, Zoom or comparable enterprise solutions. Another possibility would be a central corporate infrastructure with a secure server area that analyzes especially important meetings. The exact form depends on the use case, the available resources and the data protection requirements.

Researchers at Fraunhofer SIT first create deepfakes themselves to train the system. Shown here is an example comparison between the detection of the authentic researcher on the left and a version in which the face has been replaced with that of Tom Cruise-image above (© Fraunhofer SIT)

AI beats signal processing...“One observation we made right at the start was that traditional signal processing methods quickly reach their limits in real-time detection today. Machine learning was significantly faster and more efficient,” Steinebach reports. He feeds the self-learning system with real and manipulated audio and video data to enable it to independently recognize differences. In the audio domain, the team drew on resources including publicly available datasets containing roughly 19,000 real recordings and about 160,000 spoofed, i.e., manipulated recordings.

What is critical here is that the training data should replicate a realistic videoconferencing environment as closely as possible. Typical effects that occur in videoconferences, such as compression, fluctuating quality and blur filters, must therefore also be simulated. The AI term for this is augmentation, explains Steinebach: “We apply interference to the data such as occurs in the real world and train the system to deal with it.” Because this is a specialized model designed for a clearly defined task, the training is significantly less expansive than in large language models. “It was especially interesting that it was almost more time-consuming and difficult to create deepfakes than to detect them in a real-time system like this.”

Outlook: practical testing of technology and processes...The Fraunhofer SIT demonstrator is currently still in the proof-of-concept phase. In the next step, the researchers plan to collaborate with companies and videoconferencing system providers to reliably integrate this technology in real-world infrastructures with appropriate data protection and user-friendliness. Legal questions also have to be clarified, such as whether meeting participants should be required to consent to the analysis or how the terms of use for a videoconferencing system would reflect this function.

Martin Steinebach has mixed feelings about the future development of deepfakes. On the one hand, there is a risk that even security-critical processes such as video identification by banks could soon be falsified using deepfakes. On the other hand, the risk can be mitigated by cryptographic security, additional authentication and technical detection systems. However, it remains important to combine technology with clear processes. For example, if a bank transfer is suddenly requested during a videoconference, a second, independent communication channel should be used for verification.

© Fraunhofer SIT (sit.fraunhofer.de)