2021/01/22 by Jay Morgan, Morgan, Jay, Adeline Paiement +5
Computer Science · #Advanced Malware Detection Techniques #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Software Testing and Debugging Techniques
paper · pdf · doi:10.48550/arxiv.2101.09108
openalex publication_date 2021/01/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Deep Neural Networks (DNNs) have often supplied state-of-the-art results in pattern recognition tasks. Despite their advances, however, the existence of adversarial examples have caught the attention of the community. Many existing works have proposed methods for searching for adversarial examples within fixed-sized regions around training points. Our work complements and improves these existing approaches by adapting the size of these regions based on the problem complexity and data sampling density. This makes such approaches more appropriate for other types of data and may further improve adversarial training methods by increasing the region sizes without creating incorrect labels.