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Detection systems can achieve perfect accuracy on participant-generated deepfakes, yet the generation side reveals a troubling evasion rate that poses deployment risks.
Achieving 90% evasion against leading deepfake detectors reveals significant vulnerabilities in current detection methodologies.
A unified action embedding space across diverse humanoid robots boosts performance and interpretability, transforming how we approach robot policy learning.
