# Morphence: Moving Target Defense Against Adversarial Examples

arXiv: https://arxiv.org/abs/2108.13952 · HTML: https://mlsanity.com/paper/2108.13952/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 59 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 30 of 38)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Certified Adversarial Robustness via Randomized Smoothing](https://mlsanity.com/paper/1902.02918/) (2019)
- [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://mlsanity.com/paper/1802.00420/) (2018)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Ensemble Adversarial Training: Attacks and Defenses](https://mlsanity.com/paper/1705.07204/) (2017)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://mlsanity.com/paper/1511.04508/) (2015)
- [Stealing Machine Learning Models via Prediction APIs](https://mlsanity.com/paper/1609.02943/) (2016)
- [Provable defenses against adversarial examples via the convex outer adversarial polytope](https://mlsanity.com/paper/1711.00851/) (2017)
- [Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods](https://mlsanity.com/paper/1705.07263/) (2017)
- [Countering Adversarial Images using Input Transformations](https://mlsanity.com/paper/1711.00117/) (2017)
- [Certified Robustness to Adversarial Examples with Differential Privacy](https://mlsanity.com/paper/1802.03471/) (2018)
- [On Evaluating Adversarial Robustness](https://mlsanity.com/paper/1902.06705/) (2019)
- [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://mlsanity.com/paper/1605.07277/) (2016)
- [HopSkipJumpAttack: A Query-Efficient Decision-Based Attack](https://mlsanity.com/paper/1904.02144/) (2019)
- [Certified Defenses against Adversarial Examples](https://mlsanity.com/paper/1801.09344/) (2018)
- [Knockoff Nets: Stealing Functionality of Black-Box Models](https://mlsanity.com/paper/1812.02766/) (2018)
- [Adversarial Examples for Semantic Segmentation and Object Detection](https://mlsanity.com/paper/1703.08603/) (2017)
- [PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples](https://mlsanity.com/paper/1710.10766/) (2017)
- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [Adversarial Risk and the Dangers of Evaluating Against Weak Attacks](https://mlsanity.com/paper/1802.05666/) (2018)
- [Certified Adversarial Robustness with Additive Noise](https://mlsanity.com/paper/1809.03113/) (2018)
- [Towards Deep Neural Network Architectures Robust to Adversarial Examples](https://mlsanity.com/paper/1412.5068/) (2014)
- [Malware Detection by Eating a Whole EXE](https://mlsanity.com/paper/1710.09435/) (2017)
- [Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression](https://mlsanity.com/paper/1705.02900/) (2017)
- [Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN](https://mlsanity.com/paper/1702.05983/) (2017)
- [Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data](https://mlsanity.com/paper/1806.05476/) (2018)
- [Learning with a Strong Adversary](https://mlsanity.com/paper/1511.03034/) (2015)

## Built on by (most cited first, 9 of 9)

- [MalProtect: Stateful Defense Against Adversarial Query Attacks in ML-based Malware Detection](https://mlsanity.com/paper/2302.10739/) (2023)
- [StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection](https://mlsanity.com/paper/2202.07568/) (2022)
- [Rethinking Machine Learning Robustness via its Link with the Out-of-Distribution Problem](https://mlsanity.com/paper/2202.08944/) (2022)
- [Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution Detection](https://mlsanity.com/paper/2206.07321/) (2022)
- [The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey](https://mlsanity.com/paper/2401.05896/) (2024)
- [DYNASHIELD: A Black-Box Moving Target Defense for LLMs via Dynamic Decoding Customization](https://mlsanity.com/paper/2412.07672/) (2024)
- [Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection](https://mlsanity.com/paper/2302.00537/) (2023)
- [The Double-Edged Nature of the Rashomon Set for Trustworthy Machine Learning](https://mlsanity.com/paper/2511.21799/) (2025)
- [MorphStrata: Layer-Specific Perturbations for Generating Morphence Students in Time-Series Moving Target Defense](https://mlsanity.com/paper/2606.17435/) (2026)

## 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.