Americas, Special Report, Science

Foldit was born. Almost immediately, it began paying dividends. In this next section, we'll take a closer look at how Foldit works, what gamers have accomplished with it and whether or not they cured HIV. In Foldit, players use a simple box of tools to manipulate the shape of a protein. The idea is to bend, twist, move and shake the protein's side chains and amino acid backbones such that the whole structure is packed into its optimum shape. Players know their solution works when they get rid of collisions between side chains of atoms, hide the hydrophobic chains inside the protein, face the hydrophilic chains outward and remove large empty spaces that threaten the stability of the protein -- all of which is reflected in their score. Thermodynamics tells us that natural systems tend toward states of lower energy. Other physical laws, such as the mutual attraction of opposite charges, repulsion of like charges and limitations regarding how atomic bonds can be arranged and rotated, are also built in.|In today’s era of digital misinformation, we are increasingly faced with new threats posed by video falsification techniques. Such falsifications range from cheapfakes (e.g., lookalikes or audio dubbing) to deepfakes (e.g., sophisticated AI media synthesis methods), which are becoming perceptually indistinguishable from real videos. To tackle this challenge, we propose a multi-modal semantic forensic approach to discover clues that go beyond detecting discrepancies in visual quality, thereby handling both simpler cheapfakes and visually persuasive deepfakes. In this work, our goal is to verify that the purported person seen in the video is indeed themselves by detecting anomalous correspondences between their facial movements and the words they are saying. We leverage the idea of attribution to learn person-specific biometric patterns that distinguish a given speaker from others. We use interpretable Action Units (AUs) to capture a persons’ face and head movement as opposed to deep CNN visual features, and we are the first to use word-conditioned facial motion analysis. Unlike existing person-specific approaches, our method is also effective against attacks that focus on lip manipulation. 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