Lifeboat Foundation

In November last year, an undercover agent with the FBI was inside a group on Amazon-owned messaging app Wickr, with a name referencing young girls. The group was devoted to sharing child sexual abuse material (CSAM) within the protection of the encrypted app, which is also used by the U.S. government, journalists and activists for private communications. Encryption makes it almost impossible for law enforcement to intercept messages sent over Wickr, but this agent had found a way to infiltrate the chat, where they could start piecing together who was sharing the material.

As part of the investigation into the members of this Wickr group, the FBI used a previously unreported search warrant method to force one member to unlock the encrypted messaging app using his face. The FBI has previously forced users to unlock an iPhone with Face ID, but this search warrant, obtained by Forbes, represents the first known public record of a U.S. law enforcement agency getting a judge’s permission to unlock an encrypted messaging app with someone’s biometrics.

According to the warrant, the FBI first tracked down the suspect by sending a request for information, via an unnamed foreign law enforcement partner, to the cloud storage provider hosting the illegal images. That gave them the Gmail address the FBI said belonged to Christopher Terry, a 53-year-old Knoxville, Tennessee resident, who had prior convictions for possession of child exploitation material. It also provided IP addresses used to create the links to the CSAM. From there, investigators asked Google and Comcast via administrative subpoenas (data requests that don’t have the same level of legal requirements as search warrants) for more identifying information that helped them track down Terry and raid his home.

Deep learning techniques have recently proved to be highly promising for detecting cybersecurity attacks and determining their nature. Concurrently, many cybercriminals have been devising new attacks aimed at interfering with the functioning of various deep learning tools, including those for image classification and natural language processing.

Perhaps the most common among these attacks are adversarial attacks, which are designed to “fool” deep learning algorithms using data that has been modified, prompting them to classify it incorrectly. This can lead to the malfunctioning of many applications, biometric systems, and other technologies that operate through deep learning algorithms.

Several past studies have shown the effectiveness of different adversarial attacks in prompting deep neural networks (DNNs) to make unreliable and false predictions. These attacks include the Carlini & Wagner attack, the Deepfool attack, the fast gradient sign method (FGSM) and the Elastic-Net attack (ENA).

An artificial nose, which is combined with machine learning and built with a 16-channel sensor array was found to be able to authenticate up to 20 individuals with an average accuracy of more than 97%.

“These techniques rely on the physical uniqueness of each individual, but they are not foolproof. Physical characteristics can be copied, or even compromised by injury,” explains Chaiyanut Jirayupat, first author of the study. “Recently, human scent has been emerging as a new class of biometric authentication, essentially using your unique chemical composition to confirm who you are.”

The team turned to see if human breath could be used after finding that the skin does not produce a high enough concentration of volatile compounds for machines to detect.

Biometric authentication like fingerprint and iris scans are a staple of any spy movie, and trying to circumvent those security measures is often a core plot point. But these days the technology is not limited to spies, as fingerprint verification and facial recognition are now common features on many of our phones.

Now, researchers have developed a new potential odorous option for the biometric security toolkit: your breath. In a report published in Chemical Communications, researchers from Kyushu University’s Institute for Materials Chemistry and Engineering, in collaboration with the University of Tokyo, have developed an olfactory sensor capable of identifying individuals by analyzing the compounds in their breath.

Combined with machine learning, this “artificial nose,” built with a 16-channel sensor array, was able to authenticate up to 20 individuals with an average accuracy of more than 97%.

Wood-derived materials can be used to harvest electrical energy from everyday movements such as walking, according to University of Toronto and University of Waterloo researchers.

In a new study recently published in Nano Energy, the team demonstrated the use of lignocellulosic nanofibrils—derived from tree bark —in a prototype self-powered device capable of sending a wireless signal to a smartphone via bluetooth.

Such devices can be used to track biometric data such as heart rate, oxygen levels or skin conductivity. The innovation could improve the performance of these devices while lowering their environmental impact.

Anthony J. Ferrante, Global Head of Cybersecurity and Senior Managing Director, FTI Consulting, Inc.

Artificial intelligence (AI) models are built with a type of machine learning called deep neural networks (DNNs), which are similar to neurons in the human brain. DNNs make the machine capable of mimicking human behaviors like decision making, reasoning and problem solving. This presentation will discuss the security, ethical and privacy concerns surrounding this technology. Learning Objectives:1: Understand that the solution to adversarial AI will come from a combination of technology and policy.2: Learn that coordinated efforts among key stakeholders will help to build a more secure future.3: Learn how to share intelligence information in the cybersecurity community to build strong defenses.

The Italian privacy guarantor (GPDP) has imposed a fine of €20,000,000 on Clearview AI for implementing a biometric monitoring network in Italy without acquiring people’s consent.

This decision resulted from a proceeding that launched in February 2021, following relevant complaints about GDPR violations that stemmed directly from Clearview’s operations.

More specifically, the investigation revealed that the American facial recognition software company maintains a database of 10 billion images of people’s faces, including Italians, who had their faces extracted from public website profiles and online videos.

Rather than waiting for multiple crashes to happen at a location before intervening to improve road safety, a new study suggests we can identify dangerous areas proactively — by measuring cyclist stress levels as they navigate city streets.

The challenge: When designing a city’s transportation infrastructure, urban planners must balance the needs of drivers with the safety of pedestrians and cyclists. This is often done through surveys of local residents and best practices learned over time.

This process doesn’t always get it right, though, so sometimes cities have to install safety improvements, such as crosswalks, bike lanes, or stop lights, at dangerous intersections or stretches of road.