Enterprise AI Analysis
Hammering the Diagnosis: Rowhammer-Induced Stealthy Trojan Attacks on ViT-Based Medical Imaging
Author(s): Banafsheh Saber Latibari, Najmeh Nazari, Hossein Sayadi, Houman Homayoun, Abhijit Mahalanobis
This paper introduces Med-Hammer, a novel threat model that combines Rowhammer-based hardware fault injection with neural Trojan attacks to compromise ViT-Based medical imaging systems. It demonstrates how malicious bit flips can trigger implanted neural Trojans, leading to targeted misclassification or suppression of critical diagnoses. Experiments show high attack success rates (82.51% on MobileViT, 92.56% on Swin Transformer) while remaining stealthy. The findings highlight vulnerabilities in architectural properties and the urgent need for robust defenses spanning both model architectures and hardware platforms.
Executive Impact
Key metrics and insights from the research, highlighting critical performance, security, and efficiency implications for enterprise AI systems.
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MobileViT Attack Success Rate
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Swin Transformer Attack Success Rate
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Model Pre-training/Fine-tuning
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Strategic Bit Flips
Deep Analysis & Enterprise Applications
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Stealthy Trojan Activation
Med-Hammer enables the implantation of neural Trojans through hardware-level bit flips in memory, achieving targeted misclassification (e.g., suppressing tumor detection) without altering input scans. This stealthiness bypasses conventional defenses.
82.51% Average Attack Success RateEnterprise Process Flow
Clean Model Acquisition
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Target Parameter Identification
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Rowhammer-Based Bit Flips
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Trojan Activation & Execution
Architectural Vulnerability Across Models
Different Vision Transformer architectures exhibit varying robustness to Med-Hammer. While MobileViT and DeiT-S show significant accuracy drops, Swin-Transformer Tiny demonstrates remarkable resilience, preserving accuracy even under bit-flip attacks.
| Model | Clean Acc | Bit-flip Acc | Bit-flip + Trigger Acc |
|---|---|---|---|
| MobileViT | 91.73% | 86.83% | 82.51% |
| ResNet18 | 91.67% | 63.55% | 65.97% |
| DeiT_S | 91.28% | 10.31% | 4.2% |
| Swin-Transformer_Tiny | 91.35% | 90.97% | 92.56% |
Bit-Flip Impact: Criticality of Exponent Bits
Even a small number of bit flips can cause disproportionately large performance drops if they affect critical exponent bits. For instance, flipping 10 bits in exponent fields led to a sharp accuracy drop to 27.48%, whereas 5 bits in mantissa resulted in 89.44% accuracy. This highlights the asymmetric impact of perturbations.
Key Takeaway: The location of bit flips (e.g., exponent vs. mantissa) is more critical than the number of bit flips in determining impact on model accuracy.
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