Can New Forensic Tech Win War On AI-Generated Fake Images?

For gun lovers, the image was red meat: A Parkland school shooting survivor tearing the U.S. Constitution in two. It fed the NRA-fueled hysteria that, somehow, calling for tighter restrictions on assault weapons used in mass murders is a threat to the Second Amendment. The GIF went viral last week and conservatives went bonkers.

The problem: The animation, which looked pretty real, was fake. The teen March for Our Lives activist never put her hands on the Constitution—the animation was a doctored version of her shredding a shooting range target.

Welcome to the troubling world of “deep fakes.” Earlier this year, Reddit took down a number of forums devoted to creating bogus videos, often pornographic, featuring one person’s face swapped in for another. Open source artificial intelligence software makes the process radically simpler and more efficient than traditional video editing tools.

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Source: FastCompany

Power grid fluctuations hidden in audio recordings proved a powerful tool for police forensics

Audio and video recordings are important sources of evidence in criminal investigations, especially as more electronic devices are in use now than ever before. However, for recordings to be admissible, investigators often need to determine the time they were made, which can be difficult. Now, a team led by Vrizlynn Thing at the A*STAR Institute for Infocomm Research (I2R), in collaboration with the Singapore Police Force, has developed an impressive new system that reliably estimates the time of recordings by identifying small fluctuations in the frequency of the electrical power grid.

The 'electrical network frequency' (ENF) of power grids is centered around 50 or 60 Hertz, and is picked up in audio recordings as a background hum. The ENF shifts up and down randomly, which provides each recording with a unique fingerprint that can be compared to the long-term records captured continuously and maintained at forensic labs.

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