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Seeing Is Not Believing

Doctoring digital photos is easy. Detecting it can be hard

10 min read
Photo of Bill Gates
Illustration: Michael Elins; Photo: Bill Gates: Peter MacDiarmid/Getty Images

photo of Gates as hoboIllustration: Michael Elins; Photo: Bill Gates: Peter MacDiarmid/Ggetty Images

Just days after Sarah Palin’s selection last August as the Republican vice presidential candidate, a photo of a bikini-clad, gun-toting Palin blitzed across the Internet. Almost as quickly, it was revealed as a hoax—a crude bit of Photoshop manipulation created by splicing an image of the Alaska governor’s head onto someone else’s body. From start to finish, the doctoring probably took no more than 15 minutes.

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Image Recognition Has an Income Problem

This newly available dataset will help fix it

3 min read
robot on a laptop sitting on top of a coin with a dollar sign against a red background
iStock

Image recognition neural networks are only as good as the data they’re trained on. And that data, at least the easily available data, is heavy on images from high-income countries in Europe and North America. So, when confronted with everyday household items from lower-income countries, they get it right as little as 20 percent of the time, according to research presented in at NeruIPs 2022. But a set of training data released today by machine learning benchmarking organization MLCommons makes the image recognition neural network ResNet more than 50 percent more accurate. The goal is to “make machine learning work for everyone,” says MLCommons executive director David Kanter.

You can see the problem below. These are all stoves, even if your typical computer vision system wouldn’t always know it:

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Learn How Global Configuration Management and IBM CLM Work Together

In this presentation we will build the case for component-based requirements management

2 min read

This is a sponsored article brought to you by 321 Gang.

To fully support Requirements Management (RM) best practices, a tool needs to support traceability, versioning, reuse, and Product Line Engineering (PLE). This is especially true when designing large complex systems or systems that follow standards and regulations. Most modern requirement tools do a decent job of capturing requirements and related metadata. Some tools also support rudimentary mechanisms for baselining and traceability capabilities (“linking” requirements). The earlier versions of IBM DOORS Next supported a rich configurable traceability and even a rudimentary form of reuse. DOORS Next became a complete solution for managing requirements a few years ago when IBM invented and implemented Global Configuration Management (GCM) as part of its Engineering Lifecycle Management (ELM, formerly known as Collaborative Lifecycle Management or simply CLM) suite of integrated tools. On the surface, it seems that GCM just provides versioning capability, but it is so much more than that. GCM arms product/system development organizations with support for advanced requirement reuse, traceability that supports versioning, release management and variant management. It is also possible to manage collections of related Application Lifecycle Management (ALM) and Systems Engineering artifacts in a single configuration.

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