MergeGuard: Efficient Thwarting of Trojan Attacks in Machine Learning Models
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916723578372096 |
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| author | Shabgahi, Soheil Zibakhsh Jandali, Yaman Koushanfar, Farinaz |
| author_facet | Shabgahi, Soheil Zibakhsh Jandali, Yaman Koushanfar, Farinaz |
| contents | This paper proposes MergeGuard, a novel methodology for mitigation of AI Trojan attacks. Trojan attacks on AI models cause inputs embedded with triggers to be misclassified to an adversary's target class, posing a significant threat to model usability trained by an untrusted third party. The core of MergeGuard is a new post-training methodology for linearizing and merging fully connected layers which we show simultaneously improves model generalizability and performance. Our Proof of Concept evaluation on Transformer models demonstrates that MergeGuard maintains model accuracy while decreasing trojan attack success rate, outperforming commonly used (post-training) Trojan mitigation by fine-tuning methodologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04015 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MergeGuard: Efficient Thwarting of Trojan Attacks in Machine Learning Models Shabgahi, Soheil Zibakhsh Jandali, Yaman Koushanfar, Farinaz Cryptography and Security Artificial Intelligence This paper proposes MergeGuard, a novel methodology for mitigation of AI Trojan attacks. Trojan attacks on AI models cause inputs embedded with triggers to be misclassified to an adversary's target class, posing a significant threat to model usability trained by an untrusted third party. The core of MergeGuard is a new post-training methodology for linearizing and merging fully connected layers which we show simultaneously improves model generalizability and performance. Our Proof of Concept evaluation on Transformer models demonstrates that MergeGuard maintains model accuracy while decreasing trojan attack success rate, outperforming commonly used (post-training) Trojan mitigation by fine-tuning methodologies. |
| title | MergeGuard: Efficient Thwarting of Trojan Attacks in Machine Learning Models |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2505.04015 |