# Adversarial Learning with Margin-based Triplet Embedding Regularization

arXiv: https://arxiv.org/abs/1909.09481 · HTML: https://mlsanity.com/paper/1909.09481/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2019.

## Built on by (most cited first, 29 of 29)

- [A Survey on Long-Tailed Visual Recognition](https://mlsanity.com/paper/2205.13775/) (2022)
- [SphereFace2: Binary Classification is All You Need for Deep Face Recognition](https://mlsanity.com/paper/2108.01513/) (2021)
- [OPOM: Customized Invisible Cloak towards Face Privacy Protection](https://mlsanity.com/paper/2205.11981/) (2022)
- [AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning](https://mlsanity.com/paper/2108.06017/) (2021)
- [Enhancing Adversarial Robustness for Deep Metric Learning](https://mlsanity.com/paper/2203.01439/) (2022)
- [Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection](https://mlsanity.com/paper/2306.17431/) (2023)
- [Recent Advances in Large Margin Learning](https://mlsanity.com/paper/2103.13598/) (2021)
- [Improving the Transferability of Adversarial Attacks on Face Recognition with Beneficial Perturbation Feature Augmentation](https://mlsanity.com/paper/2210.16117/) (2022)
- [Perturbation Inactivation Based Adversarial Defense for Face Recognition](https://mlsanity.com/paper/2207.06035/) (2022)
- [Improving Visual Quality and Transferability of Adversarial Attacks on Face Recognition Simultaneously with Adversarial Restoration](https://mlsanity.com/paper/2309.01582/) (2023)
- [AdvCloak: Customized Adversarial Cloak for Privacy Protection](https://mlsanity.com/paper/2312.14407/) (2023)
- [Sound and Complete Verification of Polynomial Networks](https://mlsanity.com/paper/2209.07235/) (2022)
- [Learning with Constraint Learning: New Perspective, Solution Strategy and Various Applications](https://mlsanity.com/paper/2307.15257/) (2023)
- [Exploring Robust Features for Improving Adversarial Robustness](https://mlsanity.com/paper/2309.04650/) (2023)
- [Adversarial Attacks on Both Face Recognition and Face Anti-spoofing Models](https://mlsanity.com/paper/2405.16940/) (2024)
- [A Survey on Physical Adversarial Attacks against Face Recognition Systems](https://mlsanity.com/paper/2410.16317/) (2024)
- [Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation](https://mlsanity.com/paper/2411.15555/) (2024)
- [Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning](https://mlsanity.com/paper/2306.15457/) (2023)
- [Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval](https://mlsanity.com/paper/2312.07364/) (2023)
- [Artificial Immune System of Secure Face Recognition Against Adversarial Attacks](https://mlsanity.com/paper/2406.18144/) (2024)
- [Enhancing Generalization of Invisible Facial Privacy Cloak via Gradient Accumulation](https://mlsanity.com/paper/2401.01575/) (2024)
- [Deep Adversarial Inconsistent Cognitive Sampling for Multi-view Progressive Subspace Clustering](https://mlsanity.com/paper/2101.03783/) (2021)
- [SIRe-Networks: Convolutional Neural Networks Architectural Extension for Information Preservation via Skip/Residual Connections and Interlaced Auto-Encoders](https://mlsanity.com/paper/2110.02776/) (2021)
- [GB-CosFace: Rethinking Softmax-based Face Recognition from the Perspective of Open Set Classification](https://mlsanity.com/paper/2111.11186/) (2021)
- [F2AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns](https://mlsanity.com/paper/2310.14561/) (2023)
- [FedHide: Federated Learning by Hiding in the Neighbors](https://mlsanity.com/paper/2409.07808/) (2024)
- [A Deep Learning Framework with Geographic Information Adaptive Loss for Remote Sensing Images based UAV Self-Positioning](https://mlsanity.com/paper/2502.16164/) (2025)
- [SAP-DIFF: Semantic Adversarial Patch Generation for Black-Box Face Recognition Models via Diffusion Models](https://mlsanity.com/paper/2502.19710/) (2025)
- [VERITAS: Verification and Explanation of Realness in Images for Transparency in AI Systems](https://mlsanity.com/paper/2507.05146/) (2025)

## 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.