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Adversarial Learning with Margin-based Triplet Embedding Regularization

arXiv 1909.09481 ↗First posted 20 Sep 2019cs.CVcited by 29

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Abstract (arXiv)

The Deep neural networks (DNNs) have achieved great success on a variety of computer vision tasks, however, they are highly vulnerable to adversarial attacks. To address this problem, we propose to improve the local smoothness of the representation space, by integrating a margin-based triplet embedding regularization term into the classification objective, so that the obtained model learns to resist adversarial examples. The regularization term consists of two steps optimizations which find potential perturbations and punish them by a large margin in an iterative way. Experimental results on MNIST, CASIA-WebFace, VGGFace2 and MS-Celeb-1M reveal that our approach increases the robustness of the network against both feature and label adversarial attacks in simple object classification and deep face recognition.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Sep 2019.

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Citing papers per year: 2021: 6; 2022: 6; 2023: 8; 2024: 6; 2025: 3.

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  1. 2022A Survey on Long-Tailed Visual Recognitioncited by 75
  2. 2021SphereFace2: Binary Classification is All You Need for Deep Face Recognitioncited by 42
  3. 2022OPOM: Customized Invisible Cloak towards Face Privacy Protectioncited by 22
  4. 2021AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learningcited by 10
  5. 2022Enhancing Adversarial Robustness for Deep Metric Learningcited by 10
  6. 2023Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detectioncited by 8
  7. 2021Recent Advances in Large Margin Learningcited by 7
  8. 2022Improving the Transferability of Adversarial Attacks on Face Recognition with Beneficial Perturbation Feature Augmentationcited by 7
  9. 2022Perturbation Inactivation Based Adversarial Defense for Face Recognitioncited by 5
  10. 2023Improving Visual Quality and Transferability of Adversarial Attacks on Face Recognition Simultaneously with Adversarial Restorationcited by 5
  11. 2022Sound and Complete Verification of Polynomial Networkscited by 4
  12. 2023Learning with Constraint Learning: New Perspective, Solution Strategy and Various Applicationscited by 4
17 more
  1. 2023Exploring Robust Features for Improving Adversarial Robustnesscited by 4
  2. 2023AdvCloak: Customized Adversarial Cloak for Privacy Protectioncited by 4
  3. 2024Adversarial Attacks on Both Face Recognition and Face Anti-spoofing Modelscited by 4
  4. 2024A Survey on Physical Adversarial Attacks against Face Recognition Systemscited by 4
  5. 2023Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learningcited by 3
  6. 2024Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentationcited by 3
  7. 2023Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrievalcited by 2
  8. 2024Artificial Immune System of Secure Face Recognition Against Adversarial Attackscited by 2
  9. 2024Enhancing Generalization of Invisible Facial Privacy Cloak via Gradient Accumulationcited by 1
  10. 2021Deep Adversarial Inconsistent Cognitive Sampling for Multi-view Progressive Subspace Clusteringcited by 0
  11. 2021SIRe-Networks: Convolutional Neural Networks Architectural Extension for Information Preservation via Skip/Residual Connections and Interlaced Auto-Encoderscited by 0
  12. 2021GB-CosFace: Rethinking Softmax-based Face Recognition from the Perspective of Open Set Classificationcited by 0
  13. 2023F2AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patternscited by 0
  14. 2024FedHide: Federated Learning by Hiding in the Neighborscited by 0
  15. 2025A Deep Learning Framework with Geographic Information Adaptive Loss for Remote Sensing Images based UAV Self-Positioningcited by 0
  16. 2025SAP-DIFF: Semantic Adversarial Patch Generation for Black-Box Face Recognition Models via Diffusion Modelscited by 0
  17. 2025VERITAS: Verification and Explanation of Realness in Images for Transparency in AI Systemscited by 0