If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Machine learning models are complicated things and, often, we can have a poor understanding of how they make predictions. This can leave hidden weaknesses that could be exploited by attackers. They could trick the model into making incorrect predictions or give away sensitive information. Fake data could even be used to corrupt models without us knowing. The field of adversarial machine learning aims to address these weaknesses.
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Machine learning is a key aspect of Artificial Intelligence. However, one area that has always been an issue to worry about is adversarial attacks. It is because of this that the models trained to work in a particular way fail to do so and act in undesired ways. Computer vision is one of those areas that has grabbed eyeballs from everywhere around. This is the area where the AI systems deployed aid in processing the visual data.
Can you fool Artificial Intelligence? Three years ago, Apple launched IphoneX with cutting-edge facial recognition technology. This advanced AI technique (Face ID) replaced the old fingerprint recognition technology (Touch ID). The latest technology claimed to be more secure and robust. However, shortly after the launch of Face ID, researchers from Vietnam breached it by designing a 3D face mask.
With continued advances in science and technology, digital data have grown at an astonishing rate in various domains and forms, such as business, geography, health, multimedia, network, text, and web data. Machine learning, a powerful tool for automatically extracting, managing, inferencing, and transferring knowledge, has been proven to be extremely useful in understanding the intrinsic nature of real-world big data. Despite achieving remarkable performance, machine learning models, especially deep learning models, suffer from harassment caused by small adversarial perturbations injected by malicious parties and users. There is an immediate and crucial need for theoretical and practical techniques to identify the vulnerability of machine learning models and explore the defense mechanism and the certifiable robustness.The goal of this Research Topic is to present state-of-the-art methodologies build upon an innovative blend of techniques from computer science, mathematics, and statistics, and to greatly expand the reach of adversarial machine learning from both theoretical and practical points of view, allowing the machine learning models to be deployed in safety and security-critical applications. This Research Topic will focus on three main research tasks: (1) How to develop effective modification 'attack' strategies to tamper with intrinsic characteristics of data by injecting fake information? (2) How to develop defense strategies to offer sufficient protection to mach...
The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are.
Abstract: As more and more cyber security incident data ranging from systems logs to vulnerability scan results are collected, machine learning techniques are becoming an essential tool for real-world cyber security applications. One of the most important differences between cyber security and many other applications is the existence of malicious adversaries that actively adapt their behavior to make the existing learning models ineffective. Unfortunately, traditional learning techniques are insufficient to handle such adversarial problems directly. The adversaries adapt to the defender's reactions, and learning algorithms constructed based on the current training dataset degrades quickly. To address these concerns, we develop a game theoretic framework to model the sequential actions of the adversary and the defender, while both parties try to maximize their utilities.
What should people new to the field know about adversarial machine learning? In the first place, to understand the context of adversarial machine learning, you should know about Machine Learning and Deep Learning in general. Adversarial machine learning studies various techniques where two or more sub-components (machine learning classifiers) have an opposite reward (or loss function). Most typical applications of adversarial machine learning are: GANs and adversarial examples. You may also find applications of this approach in other machine learning papers.
Summary: What comes next after Deep Learning? How do we get to Artificial General Intelligence? Adversarial Machine Learning is an emerging space that points to that direction and shows that AGI is closer than we think. Deep Learning, Convolutional Neural Nets (CNNs) have given us dramatic improvements in image, speech, and text recognition over the last two years. They suffer from the flaw however that they can be easily fooled by the introduction of even small amounts of noise, random or intentional.
Machine learning techniques were originally designed for environments in which the training and test data are assumed to be generated from the same (although possibly unknown) distribution and/or process. In the presence of intelligent and adaptive adversaries, however, this working hypothesis is likely to be violated. This event is entirely devoted to understanding how modern machine learning methods can be applied to these adversarial environments. We will have hands-on workshops as well as talks by leading practitioners from industry and academia. Leading practitioners from Google, Capital One, Coinbase, Stripe, Square, etc. will cover their approaches to solving these problems in hands-on workshops and talks.