mlsanityData to 7 Oct 2026

Augmented Lagrangian Adversarial Attacks

arXiv 2011.11857 ↗First posted 24 Nov 2020cs.LG cs.CVcited by 28

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Abstract (arXiv)

Adversarial attack algorithms are dominated by penalty methods, which are slow in practice, or more efficient distance-customized methods, which are heavily tailored to the properties of the distance considered. We propose a white-box attack algorithm to generate minimally perturbed adversarial examples based on Augmented Lagrangian principles. We bring several algorithmic modifications, which have a crucial effect on performance. Our attack enjoys the generality of penalty methods and the computational efficiency of distance-customized algorithms, and can be readily used for a wide set of distances. We compare our attack to state-of-the-art methods on three datasets and several models, and consistently obtain competitive performances with similar or lower computational complexity.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Nov 2020.

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Citing papers per year: 2021: 3; 2022: 6; 2023: 7; 2024: 7; 2025: 3; 2026: 2.

28 papers.

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  1. 2023Glaze: Protecting Artists from Style Mimicry by Text-to-Image Modelscited by 200
  2. 2021Mind the box: l1-APGD for sparse adversarial attacks on image classifierscited by 50
  3. 2023IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AIcited by 32
  4. 2024Robust Calibration of Large Vision-Language Adapterscited by 28
  5. 2021Adversarial Robustness against Multiple and Single lp-Threat Models via Quick Fine-Tuning of Robust Classifierscited by 23
  6. 2022Proximal Splitting Adversarial Attacks for Semantic Segmentationcited by 23
  7. 2023Generative Watermarking Against Unauthorized Subject-Driven Image Synthesiscited by 21
  8. 2024Unified Physical-Digital Face Attack Detectioncited by 17
  9. 2024AttackBench: Evaluating Gradient-based Attacks for Adversarial Examplescited by 14
  10. 2022Class Adaptive Network Calibrationcited by 13
  11. 2024Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Cluescited by 8
  12. 2024σ-zero: Gradient-based Optimization of ℓ0-norm Adversarial Examplescited by 7
16 more
  1. 2022On the Minimal Adversarial Perturbation for Deep Neural Networks with Provable Estimation Errorcited by 6
  2. 2023LEAP: Efficient and Automated Test Method for NLP Softwarecited by 6
  3. 2023A Review of Adversarial Attacks in Computer Visioncited by 5
  4. 2025Benchmarking Unified Face Attack Detection via Hierarchical Prompt Tuningcited by 5
  5. 2022Robust-by-Design Classification via Unitary-Gradient Neural Networkscited by 4
  6. 2021Lagrangian Objective Function Leads to Improved Unforeseen Attack Generalization in Adversarial Trainingcited by 3
  7. 2024Class and Region-Adaptive Constraints for Network Calibrationcited by 2
  8. 2025Web Artifact Attacks Disrupt Vision Language Modelscited by 2
  9. 2022Provable Robust Saliency-based Explanationscited by 1
  10. 2023Revisiting DeepFool: generalization and improvementcited by 1
  11. 2025Fast and Flexible Robustness Certificates for Semantic Segmentationcited by 1
  12. 2022Adversarial Lagrangian Integrated Contrastive Embedding for Limited Size Datasetscited by 0
  13. 2023Robust Ranking Explanationscited by 0
  14. 2024Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Themcited by 0
  15. 2026Class Adaptive Conformal Trainingcited by 0
  16. 2026Adversarial Frontiers: Minimum-Norm Attack Ensembles for Robustness Evaluationcited by 0