论文标题
MAD-VAE：歧管意识防御变量自动编码器
MAD-VAE: Manifold Awareness Defense Variational Autoencoder
论文作者
论文摘要
尽管诸如防御工具和国防vae之类的深层生成模型在图像分类神经网络的对抗防御方面取得了重大进展，但已经发现了几种方法可以规避这些防御能力。基于国防vae，在我们的研究中，我们介绍了几种改善国防模型鲁棒性的方法。本文中引入的方法很简单，但显示出对香草防御vae的希望。通过对MNIST数据集进行广泛的实验，我们证明了算法对不同攻击的有效性。我们的实验还包括对防御模型潜在空间的攻击。我们还讨论了现有的对抗潜在空间攻击的适用性，因为它们可能存在重大缺陷。
Although deep generative models such as Defense-GAN and Defense-VAE have made significant progress in terms of adversarial defenses of image classification neural networks, several methods have been found to circumvent these defenses. Based on Defense-VAE, in our research we introduce several methods to improve the robustness of defense models. The methods introduced in this paper are straight forward yet show promise over the vanilla Defense-VAE. With extensive experiments on MNIST data set, we have demonstrated the effectiveness of our algorithms against different attacks. Our experiments also include attacks on the latent space of the defensive model. We also discuss the applicability of existing adversarial latent space attacks as they may have a significant flaw.
