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Researchers Demonstrate Two-Track Algorithm For Detecting Deepfakes

An anonymous reader quotes a report from IEEE Spectrum: 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.

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 stand out at the single-pixel level. 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. The deepfake-detecting algorithm has been described in a recent IEEE Transactions on Image Processing.


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