mlsanityData to 14 Oct 2026

Generating Adversarial Attacks in the Latent Space

arXiv 2304.04386 ↗First posted 10 Apr 2023cs.LG cs.CR cs.CVcited by 8

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Abstract (arXiv)

Adversarial attacks in the input (pixel) space typically incorporate noise margins such as L1 or L∞-norm to produce imperceptibly perturbed data that confound deep learning networks. Such noise margins confine the magnitude of permissible noise. In this work, we propose injecting adversarial perturbations in the latent (feature) space using a generative adversarial network, removing the need for margin-based priors. Experiments on MNIST, CIFAR10, Fashion-MNIST, CIFAR100 and Stanford Dogs datasets support the effectiveness of the proposed method in generating adversarial attacks in the latent space while ensuring a high degree of visual realism with respect to pixel-based adversarial attack methods.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 25 of its 34 references that are in the map. First posted on arXiv 10 Apr 2023.

Builds on

25 of 34 references.

References · 25

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  5. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  6. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  7. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  8. 2022Generative Adversarial Networkscited by 5,627
  9. 2013Intriguing properties of neural networkscited by 5,155
  10. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  11. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  12. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
13 more
  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  3. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,318
  4. 2015The Limitations of Deep Learning in Adversarial Settingscited by 790
  5. 2017One pixel attack for fooling deep neural networkscited by 582
  6. 2018Generating Adversarial Examples with Adversarial Networkscited by 258
  7. 2018Model Evaluation, Model Selection, and Algorithm Selection in Machine Learningcited by 157
  8. 2019An Exponential Learning Rate Schedule for Deep Learningcited by 121
  9. 2021A Review of Adversarial Attack and Defense for Classification Methodscited by 29
  10. 2021Deep Learning Generalization and the Convex Hull of Training Setscited by 10
  11. 2017LatentPoison - Adversarial Attacks On The Latent Spacecited by 9
  12. 2018Principal Component Analysis: A Natural Approach to Data Explorationcited by 9
  13. 2020Generating Out of Distribution Adversarial Attack using Latent Space Poisoningcited by 5

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 5.

8 papers.

  1. 2025CLIPure: Purification in Latent Space via CLIP for Adversarially Robust Zero-Shot Classificationcited by 13
  2. 2024Unsupervised Model Diagnosiscited by 4
  3. 2025Beyond Vulnerabilities: A Survey of Adversarial Attacks as Both Threats and Defenses in Computer Vision Systemscited by 4
  4. 2023Latent Magic: An Investigation into Adversarial Examples Crafted in the Semantic Latent Spacecited by 1
  5. 2025Maps for Autonomous Driving: Full-process Survey and Frontierscited by 1
  6. 2024GraphCroc: Cross-Correlation Autoencoder for Graph Structural Reconstructioncited by 0
  7. 2025LatentStealth: Unnoticeable and Efficient Adversarial Attacks on Expressive Human Pose and Shape Estimationcited by 0
  8. 2025Structured Universal Adversarial Attacks on Object Detection for Video Sequencescited by 0