Position Matters: Feature Inversion Attacks in ViT Split Inference with Token Reduction and Shuffling
Vision Transformers (ViTs) are increasingly used in split-inference systems, where edge devices transmit intermediate token representations to a remote cloud. In this setting, token reduction lowers computation and communication costs, while token shuffling disrupts the spatial organization of the transmitted tokens, potentially limiting information leakage. However, their privacy benefits remain unclear against feature inversion attacks, which attempt to reconstruct the input from the transmitted embeddings. In this work, we show that, despite disrupting the spatial structure required by conventional reconstruction attacks, transmitted token embeddings retain substantial positional infor...
| Authors | Stefano Leggio, Giulio Rossolini, Alessandro Biondi |
|---|---|
| Primary category | cs.CR |
| Published | 1 September 2026 |
| Last updated at source | 17 September 2026 |
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