Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2023
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908386752200704 |
|---|---|
| author | Chung, Ming-Yu Chou, Sheng-Yen Yu, Chia-Mu Chen, Pin-Yu Kuo, Sy-Yen Ho, Tsung-Yi |
| author_facet | Chung, Ming-Yu Chou, Sheng-Yen Yu, Chia-Mu Chen, Pin-Yu Kuo, Sy-Yen Ho, Tsung-Yi |
| contents | Dataset distillation offers a potential means to enhance data efficiency in deep learning. Recent studies have shown its ability to counteract backdoor risks present in original training samples. In this study, we delve into the theoretical aspects of backdoor attacks and dataset distillation based on kernel methods. We introduce two new theory-driven trigger pattern generation methods specialized for dataset distillation. Following a comprehensive set of analyses and experiments, we show that our optimization-based trigger design framework informs effective backdoor attacks on dataset distillation. Notably, datasets poisoned by our designed trigger prove resilient against conventional backdoor attack detection and mitigation methods. Our empirical results validate that the triggers developed using our approaches are proficient at executing resilient backdoor attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_16646 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective Chung, Ming-Yu Chou, Sheng-Yen Yu, Chia-Mu Chen, Pin-Yu Kuo, Sy-Yen Ho, Tsung-Yi Machine Learning Cryptography and Security 68T05 Dataset distillation offers a potential means to enhance data efficiency in deep learning. Recent studies have shown its ability to counteract backdoor risks present in original training samples. In this study, we delve into the theoretical aspects of backdoor attacks and dataset distillation based on kernel methods. We introduce two new theory-driven trigger pattern generation methods specialized for dataset distillation. Following a comprehensive set of analyses and experiments, we show that our optimization-based trigger design framework informs effective backdoor attacks on dataset distillation. Notably, datasets poisoned by our designed trigger prove resilient against conventional backdoor attack detection and mitigation methods. Our empirical results validate that the triggers developed using our approaches are proficient at executing resilient backdoor attacks. |
| title | Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective |
| topic | Machine Learning Cryptography and Security 68T05 |
| url | https://arxiv.org/abs/2311.16646 |