mlsanityData to 7 Oct 2026

On the Adversarial Robustness of Quantized Neural Networks

arXiv 2105.00227 ↗First posted 1 May 2021cs.LG cs.CRcited by 11

Not on the map. Why?

Abstract (arXiv)

Reducing the size of neural network models is a critical step in moving AI from a cloud-centric to an edge-centric (i.e. on-device) compute paradigm. This shift from cloud to edge is motivated by a number of factors including reduced latency, improved security, and higher flexibility of AI algorithms across several application domains (e.g. transportation, healthcare, defense, etc.). However, it is currently unclear how model compression techniques may affect the robustness of AI algorithms against adversarial attacks. This paper explores the effect of quantization, one of the most common compression techniques, on the adversarial robustness of neural networks. Specifically, we investigate and model the accuracy of quantized neural networks on adversarially-perturbed images. Results indicate that for simple gradient-based attacks, quantization can either improve or degrade adversarial robustness depending on the attack strength.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 11 of its 25 references that are in the map. First posted on arXiv 1 May 2021.

Builds on

11 of 25 references.

References · 11

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2013Intriguing properties of neural networkscited by 5,114
  3. 2018HAQ: Hardware-Aware Automated Quantization with Mixed Precisioncited by 426
  4. 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 411
  5. 2019HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precisioncited by 387
  6. 2017A Survey of Model Compression and Acceleration for Deep Neural Networkscited by 298
  7. 2017Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weightscited by 211
  8. 2019Defensive Quantization: When Efficiency Meets Robustnesscited by 95
  9. 2018A Survey on Methods and Theories of Quantized Neural Networkscited by 75
  10. 2017Attacking Binarized Neural Networkscited by 28
  11. 2020Improving Adversarial Robustness in Weight-quantized Neural Networkscited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 3; 2026: 1.

11 papers.

  1. 2024Pruning for Protection: Increasing Jailbreak Resistance in Aligned LLMs Without Fine-Tuningcited by 24
  2. 2024QGen: On the Ability to Generalize in Quantization Aware Trainingcited by 7
  3. 2023Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidencecited by 6
  4. 2023Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networkscited by 3
  5. 2023Uncovering the Hidden Cost of Model Compressioncited by 3
  6. 2025Q-MLLM: Vector Quantization for Robust Multimodal Large Language Model Securitycited by 2
  7. 2026Boundary-Aware Quantization: Finite-Scale Decision Geometry of Neural Classifierscited by 2
  8. 2023Sense, Predict, Adapt, Repeat: A Blueprint for Design of New Adaptive AI-Centric Sensing Systemscited by 1
  9. 2024David and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep Edgecited by 0
  10. 2025Quantitative Analysis of Deeply Quantized Tiny Neural Networks Robust to Adversarial Attackscited by 0
  11. 2025Hierarchical Sparse Plus Low Rank Compression of LLMcited by 0