Over the past decade, digital cameras have been widely adopted in various aspects of our society, and are being massively used in mobile phones, security surveillance, autonomous vehicles, and facial recognition. Through these cameras, enormous amounts of image data are being generated, which raises growing concerns about privacy protection.
Some existing methods address these concerns by applying algorithms to conceal sensitive information from the acquired images, such as image blurring or encryption. However, such methods still risk exposure of sensitive data because the raw images are already captured before they undergo digital processing to hide or encrypt the sensitive information. Also, the computation of these algorithms requires additional power consumption. Other efforts were also made to seek solutions to this problem by using customized cameras to downgrade the image quality so that identifiable information can be concealed. However, these approaches sacrifice the overall image quality for all the objects of interest, which is undesired, and they are still vulnerable to adversarial attacks to retrieve the sensitive information that is recorded.
A new research paper published in eLight demonstrated a new paradigm to achieve privacy-preserving imaging by building a fundamentally new type of imager designed by AI. In their paper, UCLA researchers, led by Professor Aydogan Ozcan, presented a smart camera design that images only certain types of desired objects, while instantaneously erasing other types of objects from its images without requiring any digital processing.
Apple is set to expand ads to new areas of your iPhone and iPad in search of its next big revenue driver. Also: The company slows its pace of acquiring startups, and Peloton embarks on a major overhaul.
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When Carnegie Mellon University doctoral candidates I-Hsuan Kao and Ryan Muzzio started working together a switch flicked on. Then off.
Working in the Department of Physics’ Lab for Investigating Quantum Materials, Interfaces and Devices (LIQUID) Group, Kao, Muzzio and other research partners were able to show proof of concept that running an electrical current through a novel two-dimensional material could control the magnetic state of a neighboring magnetic material without the need of applying an external magnetic field.
The groundbreaking work, which was published in Nature Materials in June and has a related patent pending, has potential applications for data storage in consumer products such as digital cameras, smartphones and laptops.
Researchers have observed the formation of 2D ice on gold surfaces that were thought to be too hydrophilic and too rough to support this type of ice.
Mobile devices use facial recognition technology to help users quickly and securely unlock their phones, make a financial transaction or access medical records. But facial recognition technologies that employ a specific user-detection method are highly vulnerable to deepfake-based attacks that could lead to significant security concerns for users and applications, according to new research involving the Penn State College of Information Sciences and Technology.
Mobile devices use facial recognition technology to help users quickly and securely unlock their phones, make a financial transaction or access medical records. But facial recognition technologies that employ a specific user-detection method are highly vulnerable to deepfake-based attacks that could lead to significant security concerns for users and applications, according to new research involving the Penn State College of Information Sciences and Technology.
The researchers found that most application programming interfaces that use facial liveness verification—a feature of facial recognition technology that uses computer vision to confirm the presence of a live user—don’t always detect digitally altered photos or videos of individuals made to look like a live version of someone else, also known as deepfakes. Applications that do use these detection measures are also significantly less effective at identifying deepfakes than what the app provider has claimed.
“In recent years we have observed significant development of facial authentication and verification technologies, which have been deployed in many security-critical applications,” said Ting Wang, associate professor of information sciences and technology and one principal investigator on the project. “Meanwhile, we have also seen substantial advances in deepfake technologies, making it fairly easy to synthesize live-looking facial images and video at little cost. We thus ask the interesting question: Is it possible for malicious attackers to misuse deepfakes to fool the facial verification systems?”
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The 200-megapixel ISOCELL HP1 sensor uses advanced levels of pixel binning technologies to extract details and light in challenging situations.
“Hello, we’ve been trying to reach you about your car’s extended warranty.” After years of seemingly unstoppable scam robocalls, this phrase is embedded into the minds of many of us. Last month the Federal Communications Commission (FCC) announced it was ordering phone providers to block any calls coming from a known car warranty robocall scam, offering hope that U.S. phone users may hear that all-too-familiar automated voice a little less often.
But there is more work required to crack down on these calls. After all, car warranty warnings are only one type of scam. To understand how robocallers reach us, and why it’s so hard to stop them, Scientific American spoke with Adam Doupé, a cybersecurity expert at Arizona State University.
[An edited transcript of the interview follows.].
An Israeli startup is using over-the-phone voice analysis to detect atrial fibrillation, a common heart condition that is hard to find on a standard screening ECG.