BadPatches: Routing-aware Backdoor Attacks on Vision Mixture of Experts
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914246377340928 |
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| author | Chan, Cedric Lintelo, Jona te Picek, Stjepan |
| author_facet | Chan, Cedric Lintelo, Jona te Picek, Stjepan |
| contents | Mixture of Experts (MoE) architectures have gained popularity for reducing computational costs in deep neural networks by activating only a subset of parameters during inference. While this efficiency makes MoE attractive for vision tasks, the patch-based processing in vision models introduces new methods for adversaries to perform backdoor attacks. In this work, we investigate the vulnerability of vision MoE models for image classification, specifically the patch-based MoE (pMoE) models and MoE-based vision transformers, against backdoor attacks. We propose a novel routing-aware trigger application method BadPatches, which is designed for patch-based processing in vision MoE models. BadPatches applies triggers on image patches rather than on the entire image. We show that BadPatches achieves high attack success rates (ASRs) with lower poisoning rates than routing-agnostic triggers and is successful at poisoning rates as low as 0.01% with an ASR above 80% on pMoE. Moreover, BadPatches is still effective when an adversary does not have complete knowledge of the patch routing configuration of the considered models. Next, we explore how trigger design affects pMoE patch routing. Finally, we investigate fine-pruning as a defense. Results show that only the fine-tuning stage of fine-pruning removes the backdoor from the model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01811 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BadPatches: Routing-aware Backdoor Attacks on Vision Mixture of Experts Chan, Cedric Lintelo, Jona te Picek, Stjepan Cryptography and Security Mixture of Experts (MoE) architectures have gained popularity for reducing computational costs in deep neural networks by activating only a subset of parameters during inference. While this efficiency makes MoE attractive for vision tasks, the patch-based processing in vision models introduces new methods for adversaries to perform backdoor attacks. In this work, we investigate the vulnerability of vision MoE models for image classification, specifically the patch-based MoE (pMoE) models and MoE-based vision transformers, against backdoor attacks. We propose a novel routing-aware trigger application method BadPatches, which is designed for patch-based processing in vision MoE models. BadPatches applies triggers on image patches rather than on the entire image. We show that BadPatches achieves high attack success rates (ASRs) with lower poisoning rates than routing-agnostic triggers and is successful at poisoning rates as low as 0.01% with an ASR above 80% on pMoE. Moreover, BadPatches is still effective when an adversary does not have complete knowledge of the patch routing configuration of the considered models. Next, we explore how trigger design affects pMoE patch routing. Finally, we investigate fine-pruning as a defense. Results show that only the fine-tuning stage of fine-pruning removes the backdoor from the model. |
| title | BadPatches: Routing-aware Backdoor Attacks on Vision Mixture of Experts |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2505.01811 |