mlsanityData to 7 Oct 2026

Morphence: Moving Target Defense Against Adversarial Examples

arXiv 2108.13952 ↗First posted 31 Aug 2021cs.LG cs.CRcited by 9

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Abstract (arXiv)

Robustness to adversarial examples of machine learning models remains an open topic of research. Attacks often succeed by repeatedly probing a fixed target model with adversarial examples purposely crafted to fool it. In this paper, we introduce Morphence, an approach that shifts the defense landscape by making a model a moving target against adversarial examples. By regularly moving the decision function of a model, Morphence makes it significantly challenging for repeated or correlated attacks to succeed. Morphence deploys a pool of models generated from a base model in a manner that introduces sufficient randomness when it responds to prediction queries. To ensure repeated or correlated attacks fail, the deployed pool of models automatically expires after a query budget is reached and the model pool is seamlessly replaced by a new model pool generated in advance. We evaluate Morphence on two benchmark image classification datasets (MNIST and CIFAR10) against five reference attacks (2 white-box and 3 black-box). In all cases, Morphence consistently outperforms the thus-far effective defense, adversarial training, even in the face of strong white-box attacks, while preserving accuracy on clean data.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 38 of its 59 references that are in the map. First posted on arXiv 31 Aug 2021.

Builds on

38 of 59 references.

References · 38

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  4. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
  5. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,259
  6. 2016Adversarial Machine Learning at Scalecited by 934
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  8. 2017Evasion Attacks against Machine Learning at Test Timecited by 686
  9. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 675
  10. 2016Stealing Machine Learning Models via Prediction APIscited by 602
  11. 2017Provable defenses against adversarial examples via the convex outer adversarial polytopecited by 564
  12. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 542
26 more
  1. 2017Countering Adversarial Images using Input Transformationscited by 517
  2. 2018Certified Robustness to Adversarial Examples with Differential Privacycited by 462
  3. 2019On Evaluating Adversarial Robustnesscited by 442
  4. 2016Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samplescited by 439
  5. 2019HopSkipJumpAttack: A Query-Efficient Decision-Based Attackcited by 328
  6. 2018Certified Defenses against Adversarial Examplescited by 305
  7. 2018Knockoff Nets: Stealing Functionality of Black-Box Modelscited by 285
  8. 2017Adversarial Examples for Semantic Segmentation and Object Detectioncited by 275
  9. 2017PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examplescited by 243
  10. 2017Deep Reinforcement Learning framework for Autonomous Drivingcited by 224
  11. 2018Adversarial Risk and the Dangers of Evaluating Against Weak Attackscited by 221
  12. 2018Certified Adversarial Robustness with Additive Noisecited by 178
  13. 2014Towards Deep Neural Network Architectures Robust to Adversarial Examplescited by 146
  14. 2017Malware Detection by Eating a Whole EXEcited by 114
  15. 2017Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compressioncited by 86
  16. 2017Generating Adversarial Malware Examples for Black-Box Attacks Based on GANcited by 74
  17. 2018Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Datacited by 69
  18. 2015Learning with a Strong Adversarycited by 68
  19. 2018Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executablescited by 60
  20. 2017Efficient Defenses Against Adversarial Attackscited by 53
  21. 2017Enhancing Robustness of Machine Learning Systems via Data Transformationscited by 38
  22. 2015Foveation-based Mechanisms Alleviate Adversarial Examplescited by 32
  23. 2019A Research Agenda: Dynamic Models to Defend Against Correlated Attackscited by 18
  24. 2019Moving Target Defense for Deep Visual Sensing against Adversarial Examplescited by 7
  25. 2020EI-MTD:Moving Target Defense for Edge Intelligence against Adversarial Attackscited by 6
  26. 2020Best-Effort Adversarial Approximation of Black-Box Malware Classifierscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 2; 2024: 2; 2025: 1; 2026: 1.

9 papers.

  1. 2023MalProtect: Stateful Defense Against Adversarial Query Attacks in ML-based Malware Detectioncited by 8
  2. 2022StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detectioncited by 3
  3. 2022Rethinking Machine Learning Robustness via its Link with the Out-of-Distribution Problemcited by 2
  4. 2022Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution Detectioncited by 2
  5. 2024The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Surveycited by 2
  6. 2024DYNASHIELD: A Black-Box Moving Target Defense for LLMs via Dynamic Decoding Customizationcited by 2
  7. 2023Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detectioncited by 0
  8. 2025The Double-Edged Nature of the Rashomon Set for Trustworthy Machine Learningcited by 0
  9. 2026MorphStrata: Layer-Specific Perturbations for Generating Morphence Students in Time-Series Moving Target Defensecited by 0