# DRAM-Locker: A General-Purpose DRAM Protection Mechanism against Adversarial DNN Weight Attacks

arXiv: https://arxiv.org/abs/2312.09027 · HTML: https://mlsanity.com/paper/2312.09027/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2023.

## Built on by (most cited first, 2 of 2)

- [Hessian-aware Training for Enhancing DNNs Resilience to Parameter Corruptions](https://mlsanity.com/paper/2504.01933/) (2025)
- [EIM-TRNG: Obfuscating Deep Neural Network Weights with Encoding-in-Memory True Random Number Generator via RowHammer](https://mlsanity.com/paper/2507.02206/) (2025)

## Abstract (arXiv)

In this work, we propose DRAM-Locker as a robust general-purpose defense mechanism that can protect DRAM against various adversarial Deep Neural Network (DNN) weight attacks affecting data or page tables. DRAM-Locker harnesses the capabilities of in-DRAM swapping combined with a lock-table to prevent attackers from singling out specific DRAM rows to safeguard DNN's weight parameters. Our results indicate that DRAM-Locker can deliver a high level of protection downgrading the performance of targeted weight attacks to a random attack level. Furthermore, the proposed defense mechanism demonstrates no reduction in accuracy when applied to CIFAR-10 and CIFAR-100. Importantly, DRAM-Locker does not necessitate any software retraining or result in extra hardware burden.