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主题： GANs in computer vision: Introduction to generative learning
主要内容： 在这个综述系列文章中，我们将重点讨论计算机视觉应用程序的大量GANs。具体地说，我们将慢慢地建立在导致产生性对抗网络(GAN)进化的思想和原则之上。我们将遇到不同的任务，如条件图像生成，3D对象生成，视频合成。
目录：
- 对抗学习
- GAN(生成对抗网络)
- 条件生成对抗网
- 基于深度卷积
- 生成对抗网络的无监督表示学习
- Info GAN: Info最大化生成对抗网的表征学习
一般来说，数据生成方法存在于各种各样的现代深度学习应用中，从计算机视觉到自然语言处理。在这一点上，我们可以用肉眼生成几乎无法区分的生成数据。生成性学习大致可分为两大类:a)变分自编码器(VAE)和b)生成性对抗网络(GAN)。
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Many users implicitly assume that software can only be exploited after it is installed. However, recent supply-chain attacks demonstrate that application integrity must be ensured during installation itself. We introduce SIGL, a new tool for detecting malicious behavior during software installation. SIGL collects traces of system call activity, building a data provenance graph that it analyzes using a novel autoencoder architecture with a graph long short-term memory network (graph LSTM) for the encoder and a standard multilayer perceptron for the decoder. SIGL flags suspicious installations as well as the specific installation-time processes that are likely to be malicious. Using a test corpus of 625 malicious installers containing real-world malware, we demonstrate that SIGL has a detection accuracy of 96%, outperforming similar systems from industry and academia by up to 87% in precision and recall and 45% in accuracy. We also demonstrate that SIGL can pinpoint the processes most likely to have triggered malicious behavior, works on different audit platforms and operating systems, and is robust to training data contamination and adversarial attack. It can be used with application-specific models, even in the presence of new software versions, as well as application-agnostic meta-models that encompass a wide range of applications and installers.
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Many users implicitly assume that software can only be exploited after it is installed. However, recent supply-chain attacks demonstrate that application integrity must be ensured during installation itself. We introduce SIGL, a new tool for detecting malicious behavior during software installation. SIGL collects traces of system call activity, building a data provenance graph that it analyzes using a novel autoencoder architecture with a graph long short-term memory network (graph LSTM) for the encoder and a standard multilayer perceptron for the decoder. SIGL flags suspicious installations as well as the specific installation-time processes that are likely to be malicious. Using a test corpus of 625 malicious installers containing real-world malware, we demonstrate that SIGL has a detection accuracy of 96%, outperforming similar systems from industry and academia by up to 87% in precision and recall and 45% in accuracy. We also demonstrate that SIGL can pinpoint the processes most likely to have triggered malicious behavior, works on different audit platforms and operating systems, and is robust to training data contamination and adversarial attack. It can be used with application-specific models, even in the presence of new software versions, as well as application-agnostic meta-models that encompass a wide range of applications and installers.
