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Breakthrough U.S. Army Technology is Game Changer for Deepfake Detection

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Impression of a deepfake.

U.S. Army researchers have developed a deepfake detection method to assist soldiers with target detection and recognition and semantic scene understanding.

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Researchers at the U.S. Army Combat Capabilities Development Command's Army Research Laboratory and the University of Southern California (USC) have developed a deepfake detection method for supporting mission-essential tasks.

The team said DefakeHop's core innovation is Successive Subspace Learning (SSL), a signal representation and transform theory designed as a neural network architecture.

USC's C.-C. Jay Kuo described SSL as "a complete data-driven unsupervised framework [that] offers a brand new tool for image processing and understanding tasks such as face biometrics."

Among DefakeHop's purported advantages over current state-of-the-art deepfake video detection methods are mathematical transparency, less complexity, and robustness against adversarial attacks.

From U.S. Army Research Laboratory
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