# Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss

arXiv: https://arxiv.org/abs/1905.11713 · HTML: https://mlsanity.com/paper/1905.11713/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

## Built on by (most cited first, 13 of 13)

- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart](https://mlsanity.com/paper/2105.14785/) (2021)
- [Learning Diverse-Structured Networks for Adversarial Robustness](https://mlsanity.com/paper/2102.01886/) (2021)
- [What You See is Not What the Network Infers: Detecting Adversarial Examples Based on Semantic Contradiction](https://mlsanity.com/paper/2201.09650/) (2022)
- [AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning](https://mlsanity.com/paper/2108.06017/) (2021)
- [Exploring Adversarial Robustness of Deep Metric Learning](https://mlsanity.com/paper/2102.07265/) (2021)
- [Improving Transformation-based Defenses against Adversarial Examples with First-order Perturbations](https://mlsanity.com/paper/2103.04565/) (2021)
- [Noisy Learning for Neural ODEs Acts as a Robustness Locus Widening](https://mlsanity.com/paper/2206.08237/) (2022)
- [AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation](https://mlsanity.com/paper/2404.12635/) (2024)
- [Enhancing robustness of data-driven SHM models: adversarial training with circle loss](https://mlsanity.com/paper/2406.14232/) (2024)
- [Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness](https://mlsanity.com/paper/2412.19947/) (2024)
- [GAN-enhanced Simulation-driven DNN Testing in Absence of Ground Truth](https://mlsanity.com/paper/2503.15953/) (2025)
- [Low Rank Adaptation for Adversarial Perturbation](https://mlsanity.com/paper/2604.27487/) (2026)

## Abstract (arXiv)

Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial examples. In this paper, we improve the robustness of DNNs by utilizing techniques of Distance Metric Learning. Specifically, we incorporate Triplet Loss, one of the most popular Distance Metric Learning methods, into the framework of adversarial training. Our proposed algorithm, Adversarial Training with Triplet Loss (AT2L), substitutes the adversarial example against the current model for the anchor of triplet loss to effectively smooth the classification boundary. Furthermore, we propose an ensemble version of AT2L, which aggregates different attack methods and model structures for better defense effects. Our empirical studies verify that the proposed approach can significantly improve the robustness of DNNs without sacrificing accuracy. Finally, we demonstrate that our specially designed triplet loss can also be used as a regularization term to enhance other defense methods.