论文标题
通过提高网络容量来改善深度学习模型的鲁棒性，以防止对抗性攻击
Improving Deep Learning Model Robustness Against Adversarial Attack by Increasing the Network Capacity
论文作者
论文摘要
如今，我们越来越依赖深度学习（DL）模型，因此必须保护这些系统的安全至关重要。本文通过使用实验，探讨了深度学习和分析中的安全问题，以构建更多弹性模型的前进之路。进行了实验，以确定一种新方法的优势和劣势，以改善DL模型针对对抗攻击的鲁棒性。结果表明，改进和新想法可以用作研究人员和从业人员创建越来越更好的DL算法的建议。
Nowadays, we are more and more reliant on Deep Learning (DL) models and thus it is essential to safeguard the security of these systems. This paper explores the security issues in Deep Learning and analyses, through the use of experiments, the way forward to build more resilient models. Experiments are conducted to identify the strengths and weaknesses of a new approach to improve the robustness of DL models against adversarial attacks. The results show improvements and new ideas that can be used as recommendations for researchers and practitioners to create increasingly better DL algorithms.
