Disinformation Visualizer

Updated on
Mar 05, 2021

This project visualizes the Atlantic Council’s DFRLab research on coordinated disinformation campaigns. Coordinated disinformation campaigns are more likely to thrive when they go unnoticed and unchecked. This interactive visualizer breaks down the methods, targets, and origins of select coordinated disinformation campaigns throughout the world. There are significant efforts across the industry working to stop the effects of disinformation. These countermeasures take a wide range of forms. At Jigsaw, we have been working with teams of researchers across Google and academia to test new technology for detecting manipulated media.

How Detectors Work
When an image is manipulated—such as merging two images together or deleting something from the background—the image may leave behind traces of that manipulation. Detectors work by training algorithms or using machine learning to identify these traces, indicating where and how an image has been manipulated. Slide the bar over to see what has been added to this image. “Splicebuster,” a detector from the University Federico II of Naples, attempts to compare “noise” patterns in different parts of an image to see if more than one camera (make and model) was used to create an image. Jigsaw is working on an experimental technology called Assembler that brings together detectors from across the academic community with the goal of making it easier to identify image manipulations. Deepfake Detection Research Manipulated and synthetic videos, including deepfakes, are an emerging tactic for disinformation that poses a threat to news organizations and society at large. In order to build ways to detect deepfakes, we partnered with Google Research to create a dataset of deepfakes to help the research community develop machine learning techniques to detect when a video has been manipulated. We made that dataset available to the academic research community to help support efforts to develop detection methods. The Technical University of Munich and the University Federico II of Naples have incorporated this dataset into their new FaceForensics benchmark, which gives researchers a dataset with which to measure their models against. The field is moving quickly and the threat will evolve. To counter it, we plan to continue the collaboration with the research and technology community to improve our own technology and expand our learnings.

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