Tag Archives: Computing/Software

IBM and Linux Call on Developers to Make Natural Disasters Less Deadly

Post Syndicated from Lynne Peskoe-Yang original https://spectrum.ieee.org/tech-talk/computing/software/ibm-and-linux-ask-developers-to-build-tech-that-makes-natural-disasters-less-deadly

The international Call for Code competition encourages developers to invent new technologies that can assist people after a hurricane or flood

On a stormy Tuesday in July, a group of 30 young programmers gathered in New York City to take on natural disasters. The attendees—most of whom were current college students and alumnae of the nonprofit Girls Who Code—had signed up for a six-hour hackathon in the middle of summer break.

Flash floods broke out across the city, but the atmosphere in the conference room remained upbeat. The hackathon was hosted in the downtown office of IBM as one of the final events in this year’s Call for Code challenge, a global competition sponsored by IBM and Linux. The challenge focuses on using technology to assist survivors of catastrophes including tropical storms, fires, and earthquakes. 

Recent satellite hackathon events in the 2019 competition have recruited developers in Cairo to address Egypt’s national water shortage; in Paris to brainstorm AI solutions for rebuilding the Notre Dame cathedral; and in Bayamón, Puerto Rico, to improve resilience in the face of future hurricanes. 

A Two-Track Algorithm To Detect Deepfake Images

Post Syndicated from Mark Anderson original https://spectrum.ieee.org/tech-talk/computing/software/a-twotrack-algorithm-to-detect-deepfake-images

A neural-network-based tool can spot image manipulation at the level of single-pixels

Journal Watch report logo, link to report landing page

Researchers have demonstrated a new algorithm for detecting so-called deepfake images—those altered imperceptibly by AI systems, potentially for nefarious purposes. Initial tests of the algorithm picked out phony from undoctored images down to the individual pixel level with between 71 and 95 percent accuracy, depending on the sample data set used. The algorithm has not yet been expanded to include the detection of deepfake videos.

Deepfakes “are images or videos that have been doctored—either you insert something into it or remove something out of it—so it changes the meaning of the picture,” says Amit Roy-Chowdhury, professor of electrical and computer engineering at the University of California, Riverside. The challenge arises because it’s done “in a way so that to the human eye it’s not obvious immediately that it has been manipulated.”

In rapidly developing situations, such as an humanitarian crisis, a business’s product launch, or an election campaign, deepfake videos and images could alter how events play out. Imagine a doctored image in which a political candidate was supposedly committing a violent crime, or a doctored video in which a CEO supposedly confesses to concealing safety problems with her company’s signature product line.

Chowdhury is one of five authors of the deepfake-detecting algorithm, described in a recent IEEE Transactions on Image Processing. He says such detection algorithms could be a powerful tool to fight this new menace of the social media age. But people also need to be careful not to become over-dependent on these algorithms either, he warns. An overly trusted detection algorithm that can be tricked could be weaponized by those seeking to spread false information. A deepfake crafted to exploit a trusted algorithm’s particular weaknesses could effectively result in the algorithm blessing the fake with a certificate of authenticity in the minds of experts, journalists and the public, rendering it even more damaging.

“I think we have to be careful in anything that has to do with AI and machine learning today,” Roy-Chowdhury says. “We need to understand that the results these systems give are probabilistic. And very often the probabilities are not in the range of 0.98 or 0.99. They’re much lower than that. We should not accept them on blind faith. These are hard problems.”

In that sense, he says, deepfakes are really just a new frontier in cybersecurity. And cybersecurity is a perpetual arms race with bad guys and good guys each making advances in often incremental steps.

Roy-Chowdhury says that with their latest work his group has harnessed a set of concepts that already exist separately in the literature, but which they have combined in novel and potentially powerful way.

One component of the algorithm is a variety of a so-called “recurrent neural network,” which splits the image in question into small patches and looks at those patches pixel by pixel. The neural network has been trained by letting it examine thousands of both deepfake and genuine images, so it has learned some of the qualities that make fakes at stand out at the single-pixel level.

Roy-Chowdhury says the boundaries around the doctored portion of an image are often what contain telltale signs of manipulation. “If an object is inserted, it is often the boundary regions that have certain characteristics,” he says. “So the person who’s tampering the image will probably try to do it so that the boundary is very smooth. What we found out is the tampered images were often smoother than in natural images. Because the person who did the manipulation was going out of his way to make it very smooth.”

Another portion of the algorithm, on a parallel track to the part looking at single pixels, passes the whole image through a series of encoding filters—almost as if it were performing an image compression, as when you click the “compress image” box when saving a TIFF or a JPEG. These filters, in a mathematical sense, enable the algorithm to consider the entire image at larger, more holistic levels.

The algorithm then compares the output of the pixel-by-pixel and higher-level encoding filter analyses. When these parallel analyses trigger red flags over the same region of an image, it is then tagged as a possible deepfake.

For example, say that a stock image of a songbird has been pasted onto a picture of an empty tree branch. The pixel-by-pixel algorithm in this case might flag the pixels around the bird’s claws as problematic, while the encoder algorithm might spot patterns in the larger image (noticing, perhaps, other boundary problems or anomalies at the larger-scale level). So long as both of these neural nets flagged the same region of the image around the bird, then Roy-Chowdhury’s group’s algorithm would categorize the bird-and-branch photo as a possible deepfake.

Roy-Chowdhury says that the algorithm now needs to be expanded to handle video. Such a next-level algorithm, he says, would potentially include how the image evolves frame-by-frame and whether any detectable patterns can be discerned from that evolution in time.

Given the urgency of deepfake detection, as hostile actors around the world increasingly seek to manipulate political events using false information, Roy-Chowdhury encourages researchers to contact his group for code or pointers toward further developing this algorithm for deepfake detection in the wild.

How to Protect Enterprise Systems with Cloud-Based Firewalls

Post Syndicated from IEEE Spectrum Recent Content full text original https://spectrum.ieee.org/webinar/how-to-protect-enterprise-systems-with-cloudbased-firewalls

In this webcast SANS analyst Kevin Garvey explores key features of cloud-based firewalls and how they differ from more traditional firewalls.

As organizations begin their migrations to the cloud, security needs to meet the evolving challenges. Cloud-based firewalls are a key part of those security plans. In this webcast SANS analyst Kevin Garvey explores key features of cloud-based firewalls and how they differ from more traditional firewalls, the ease with which organizations can manage firewalls in AWS, and advanced features of firewalls that are of significant value to users’ organizations.

Attendees will learn how:

  • Web filtering, network logging, intrusion detection and prevention systems, single sign-on and authentication support, and deep packet inspection function in a cloud-based environment
  • Easily they can manage firewalls through APIs, AWS CloudFormation and independent software vendors
  • Features such as behavioral threat detection and next-generation analytics can enhance the security that firewalls provide
  • A firewall can be deployed and advanced features enabled in an EC2 instance

Get Keysight’s Basic Instruments Flyer Featuring the New N6790 series Electronic Loads

Post Syndicated from IEEE Spectrum Recent Content full text original https://spectrum.ieee.org/whitepaper/learn-about-keysights-next-generation-n6790-series-electronic-loads

Get Keysight’s Basic Instruments Flyer Featuring the New N6790 series Electronic Loads

Power technology has evolved drastically for power sources while electronic load capability has lagged, negatively impacting production schedules, cost of test, and product quality. This Basic Instruments flyer highlights Keysight’s next generation electronic loads, allowing for a complete DC power conversion solution on the popular N6700 modular power system.


Revolutionize Your Design and Test Workflow

Post Syndicated from IEEE Spectrum Recent Content full text original https://spectrum.ieee.org/whitepaper/want-to-innovate-with-testops-learn-how

Revolutionize Your Design and Test Workflow

Agile software development profoundly transformed software development in the 1900s. Far more than a process; Agile created a new way to work.

Today, a similar transformation is happening in test and measurement. TestOps is an innovative approach to product design and test which improves workflow efficiency and speeds product time to market.

Learn more about TestOps and how to accelerate your product development workflow.


AI Can Edit Photos With Zero Experience

Post Syndicated from Matthew Hutson original https://spectrum.ieee.org/tech-talk/computing/software/ai-can-edit-photos-with-zero-experience

A new technique called Double-DIP deploys deep learning to polish images without prior training

Imagine showing a photo taken through a storefront window to someone who has never opened her eyes before, and asking her to point to what’s in the reflection and what’s in the store. To her, everything in the photo would just be a big jumble. Computers can perform image separations, but to do it well, they typically require handcrafted rules or many, many explicit demonstrations: here’s an image, and here are its component parts.

New research finds that a machine-learning algorithm given just one image can discover patterns that allow it to separate the parts you want from the parts you don’t. The multi-purpose method might someday benefit any area where computer vision is used, including forensics, wildlife observation, and artistic photo enhancement.

Many tasks in machine learning require massive amounts of training data, which is not always available. A team of Israeli researchers is exploring what they call “deep internal learning,” where software figures out the internal structure of a single image from scratch. Their new work builds on a recent advance from another group called DIP, or Deep Image Prior. (Spoiler: The new method is called Double-DIP.)

EMI step-by-step guide from Rohde & Schwarz- Download For Free

Post Syndicated from IEEE Spectrum Recent Content full text original https://spectrum.ieee.org/whitepaper/emi-stepbystep-guide-from-rohde-schwarz-download-for-free

Be able to discover & analyze EMI in a more systematic & methodical approach to solve your problems.

In our free step-by-step guide, we break down the whole EMI design test process into “Locate”, “Capture”, and “Analyze”. Download & learn more.


Tips and Tricks on How to Verify Control Loop Stability

Post Syndicated from IEEE Spectrum Recent Content full text original https://spectrum.ieee.org/whitepaper/tips-and-tricks-on-how-to-verifying-control-loop-stability

Register for our Application Note “Tips and Tricks on how to verify control loop stability”

The Application Note explains the main measurement concept and will guide the user during the measurements and mention the main topics in a practical manner. Wherever possible, a hint is given where the user should pay attention.


JumpStart Guide to Cloud-Based Firewalls in AWS

Post Syndicated from IEEE Spectrum Recent Content full text original https://spectrum.ieee.org/webinar/jumpstart-guide-to-cloudbased-firewalls-in-aws

In this webinar: SANS, Optiv, and AWS Marketplace will lead an in-depth exploration of the key issues to consider when choosing next-generation firewall/threat prevention solutions for integration into a cloud environment, as well as recommend a process for making that important decision.

In this webinar:

SANS, Optiv, and AWS Marketplace will lead an in-depth exploration of the key issues to consider when choosing next-generation firewall/threat prevention solutions for integration into a cloud environment, as well as recommend a process for making that important decision.

Attendees will learn:

·         How cloud design affects the selection and use of next-generation firewalls and threat protection capabilities

·         Needs and capabilities associated with firewalls and threat prevention capabilities, including intrusion prevention, antivirus, logging and alerting, event correlation, continuous dynamic updating of threat databases, and malware protection

·         Business, technical, and operational considerations for cloud-based firewall protection, including AWS-specific considerations and real-world success observations

 Key questions for potential vendors to determine which products are well-suited for integration and implementation in your AWS environment

GitHub Releases New Tools to Report Vulnerabilities

Post Syndicated from Rina Diane Caballar original https://spectrum.ieee.org/tech-talk/computing/software/github-releases-new-tools-to-report-vulnerabilities

The new features came out the same day as a study that found many open-source projects lack a clear way to report security problems

For most software developers, importing code from third-party libraries is an easy way to add new functionalities to a program without building those features from scratch. But relying on open-source libraries can be risky, as hackers often target security vulnerabilities within them.

Given all this, it’s important for users of any library to be able to report potential security issues to the project’s owners, so such problems can be fixed before they’re exploited. But until recently, many projects on the online repository GitHub lacked a clear way for users to submit security reports.

“I think reporting is the first step needed,” says University of Waterloo assistant professor Meiyappan Nagappan. But, adds University of Michigan professor Atul Prakash, “if the reporting process isn’t simple and straightforward, that can discourage or delay security reporting. And that can have consequences.”

While working on another project in 2018, Nagappan and his team found it difficult to report a vulnerable version of Apache Struts, the open-source library hackers exploited to breach Equifax in 2017. They tried informing other GitHub projects with the same dependency through a combination of emailing project owners, opening issues, and submitting pull requests.