mlsanityData to 7 Oct 2026

DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples

arXiv 1702.06763 ↗First posted 22 Feb 2017cs.LG cs.AI cs.CRcited by 22

Not on the map. Why?

Abstract (arXiv)

Recent studies have shown that deep neural networks (DNN) are vulnerable to adversarial samples: maliciously-perturbed samples crafted to yield incorrect model outputs. Such attacks can severely undermine DNN systems, particularly in security-sensitive settings. It was observed that an adversary could easily generate adversarial samples by making a small perturbation on irrelevant feature dimensions that are unnecessary for the current classification task. To overcome this problem, we introduce a defensive mechanism called DeepCloak. By identifying and removing unnecessary features in a DNN model, DeepCloak limits the capacity an attacker can use generating adversarial samples and therefore increase the robustness against such inputs. Comparing with other defensive approaches, DeepCloak is easy to implement and computationally efficient. Experimental results show that DeepCloak can increase the performance of state-of-the-art DNN models against adversarial samples.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Feb 2017.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 9; 2022: 5; 2023: 4; 2024: 2; 2025: 2.

22 papers.

Sort:

  1. 2022Sparsity Winning Twice: Better Robust Generalization from More Efficient Trainingcited by 41
  2. 2021Generative Dynamic Patch Attackcited by 19
  3. 2023Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identificationcited by 19
  4. 2024Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Maskingcited by 19
  5. 2021A Unified Game-Theoretic Interpretation of Adversarial Robustnesscited by 17
  6. 2022Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Freecited by 15
  7. 2022Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustnesscited by 10
  8. 2021Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspectivecited by 9
  9. 2022A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunitiescited by 8
  10. 2024Machine Learning Robustness: A Primercited by 8
  11. 2021Adversarial Attacks and Defenses in Physiological Computing: A Systematic Reviewcited by 7
  12. 2021Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practicecited by 6
10 more
  1. 2025Hierarchical Testing with Rabbit Optimization for Industrial Cyber-Physical Systemscited by 4
  2. 2021DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learningcited by 2
  3. 2023A Random-patch based Defense Strategy Against Physical Attacks for Face Recognition Systemscited by 2
  4. 2021Improving Global Adversarial Robustness Generalization With Adversarially Trained GANcited by 1
  5. 2021A Unified Game-Theoretic Interpretation of Adversarial Robustnesscited by 0
  6. 2021Benign Adversarial Attack: Tricking Models for Goodnesscited by 0
  7. 2022Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial Pruningcited by 0
  8. 2023RobustPdM: Designing Robust Predictive Maintenance against Adversarial Attackscited by 0
  9. 2023Improving Robustness of Deep Convolutional Neural Networks via Multiresolution Learningcited by 0
  10. 2025Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Reviewcited by 0