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StyleAT: Defending Face Recognition Against Semantic Attacks
artificial-intelligencecomputer-vision-and-pattern-recognitioncryptography-and-securitymachine-learning
With face-recognition models now embedded in everyday authentication and surveillance, recent works have pinpointed a critical weakness: these models remain acutely vulnerable to adversarial semantic edits. I.e., adversarially produced semantic alterations to the input, such as slight aging or pose
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