Osmosis Distillation: Model Hijacking with the Fewest Samples
Transfer learning is devised to leverage knowledge from pre-trained models to solve new tasks with limited data and computational resources. Meanwhile, dataset distillation has emerged to synthesize a compact dataset that preserves critical information from the original large dataset. Therefore, a combination of transfer learning and dataset distillation offers promising performance in evaluations. However, a non-negligible security threat remains undiscovered in transfer learning using synthetic datasets generated by dataset distillation methods, where an adversary can perform a model hijacking attack with only a few poisoned samples in the synthetic dataset. To reveal this threat, we pr...
| Authors | Yuchen Shi, Huajie Chen, Heng Xu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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