MemLoss: Enhancing Adversarial Training with Recycling Adversarial Examples

2025/10/10 by Mahdi, Soroush, Maryam Amirmazlaghani, Saeed Saravani +4
Computer Science · #Advanced Malware Detection Techniques #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Digital and Cyber Forensics #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2510.09105

openalex publication_date 2025/10/10 · openalex created_date 2025/10/17 · openalex updated_date 2026/07/28

Abstract

In this paper, we propose a new approach called MemLoss to improve the adversarial training of machine learning models. MemLoss leverages previously generated adversarial examples, referred to as 'Memory Adversarial Examples,' to enhance model robustness and accuracy without compromising performance on clean data. By using these examples across training epochs, MemLoss provides a balanced improvement in both natural accuracy and adversarial robustness. Experimental results on multiple datasets, including CIFAR-10, demonstrate that our method achieves better accuracy compared to existing adversarial training methods while maintaining strong robustness against attacks.

Citations

• Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
• Overfitting in adversarially robust deep learning
• Unlabeled Data Improves Adversarial Robustness
• Theoretically Principled Trade-off between Robustness and Accuracy
• Towards Deep Learning Models Resistant to Adversarial Attacks
• Regularizing deep networks using efficient layerwise adversarial training
• Towards Evaluating the Robustness of Neural Networks
• Deep Residual Learning for Image Recognition
• Explaining and Harnessing Adversarial Examples
• Intriguing properties of neural networks

Related

• Efficient Adversarial Training with Transferable Adversarial Examples
• Adversarial Machine Learning at Scale
• Towards Model-Agnostic Adversarial Defenses using Adversarially Trained Autoencoders
• MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks
• Ensemble Adversarial Training: Attacks and Defenses