mlsanityData to 7 Oct 2026

Adversarial Attack Based on Prediction-Correction

arXiv 2306.01809 ↗First posted 2 Jun 2023cs.CR cs.AI cs.CVcited by 7

Not on the map. Why?

Abstract (arXiv)

Deep neural networks (DNNs) are vulnerable to adversarial examples obtained by adding small perturbations to original examples. The added perturbations in existing attacks are mainly determined by the gradient of the loss function with respect to the inputs. In this paper, the close relationship between gradient-based attacks and the numerical methods for solving ordinary differential equation (ODE) is studied for the first time. Inspired by the numerical solution of ODE, a new prediction-correction (PC) based adversarial attack is proposed. In our proposed PC-based attack, some existing attack can be selected to produce a predicted example first, and then the predicted example and the current example are combined together to determine the added perturbations. The proposed method possesses good extensibility and can be applied to all available gradient-based attacks easily. Extensive experiments demonstrate that compared with the state-of-the-art gradient-based adversarial attacks, our proposed PC-based attacks have higher attack success rates, and exhibit better transferability.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 31 of its 44 references that are in the map. First posted on arXiv 2 Jun 2023.

Builds on

31 of 44 references.

References · 31

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  4. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  5. 2013Intriguing properties of neural networkscited by 5,114
  6. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  7. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  8. 2016Adversarial examples in the physical worldcited by 1,762
  9. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
  10. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
  11. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
  12. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,259
19 more
  1. 2017Ensemble Adversarial Training: Attacks and Defensescited by 833
  2. 2018Improving Transferability of Adversarial Examples with Input Diversitycited by 591
  3. 2016Delving into Transferable Adversarial Examples and Black-box Attackscited by 530
  4. 2019Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attackscited by 440
  5. 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 411
  6. 2017Mitigating Adversarial Effects Through Randomizationcited by 382
  7. 2019Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attackscited by 352
  8. 2017Adversarial Examples: Attacks and Defenses for Deep Learningcited by 350
  9. 2017Defense against Adversarial Attacks Using High-Level Representation Guided Denoisercited by 329
  10. 2018Feature Denoising for Improving Adversarial Robustnesscited by 312
  11. 2018Generating Adversarial Examples with Adversarial Networkscited by 256
  12. 2021Enhancing the Transferability of Adversarial Attacks through Variance Tuningcited by 242
  13. 2015DeepDriving: Learning Affordance for Direct Perception in Autonomous Drivingcited by 229
  14. 2018ComDefend: An Efficient Image Compression Model to Defend Adversarial Examplescited by 104
  15. 2018Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examplescited by 98
  16. 2020Towards Transferable Adversarial Attack against Deep Face Recognitioncited by 79
  17. 2018Understanding and Enhancing the Transferability of Adversarial Examplescited by 41
  18. 2021Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fittingcited by 37
  19. 2019A Robust Approach for Securing Audio Classification Against Adversarial Attackscited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 1; 2026: 2.

7 papers.

  1. 2024Securely Fine-tuning Pre-trained Encoders Against Adversarial Examplescited by 21
  2. 2024GADT: Enhancing Transferable Adversarial Attacks through Gradient-guided Adversarial Data Transformationcited by 2
  3. 2024On the Adversarial Transferability of Generalized "Skip Connections"cited by 1
  4. 2026Towards Robust Personalized Federated Learning: Vulnerability Assessment and Defense Co-Designcited by 1
  5. 2024Longitudinal Mammogram Exam-based Breast Cancer Diagnosis Models: Vulnerability to Adversarial Attackscited by 0
  6. 2025Boosting Adversarial Transferability Against Defenses via Multi-Scale Transformationcited by 0
  7. 2026Devling into Adversarial Transferability on Image Classification: Review, Benchmark, and Evaluationcited by 0