Cheng Hong

DeepInvert: Semi-Supervised Embedding Inversion Against Obfuscated Language Models

This work presents DeepInvert, a semi-supervised embedding inversion attack that recovers original tokens from obfuscated representations with higher accuracy than prior methods, and reveals a task-dependent tension: obfuscation schemes preserving enough signal for utility also retain sufficient structure for inversion, while schemes resisting inversion collapse utility.