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Adversarial Robustness For Machine Learning Models

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Adversarial Robustness for Machine Learning Models

Adversarial Robustness for Machine Learning Models Book
Author : Pin-Yu Chen,Cho-Jui Hsieh
Publisher : Academic Press
Release : 2022-09-15
ISBN : 9780128240205
Language : En, Es, Fr & De

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Book Description :

While machine learning (ML) algorithms have achieved remarkable performance in many applications, recent studies have demonstrated their lack of robustness against adversarial disturbance. The lack of robustness brings security concerns in ML models for real applications such as self-driving cars, robotics controls and healthcare systems. Adversarial robustness has become one of the mainstream topics in machine learning with much research carried out, while many companies have started to incorporate security and robustness into their systems. Adversarial Robustness for Machine Learning Models summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense, and veri?cation. It contains 6 parts: The ?rst three parts cover adversarial attack, veri?cation, and defense, mainly focusing on image classi?cation applications, which is the standard benchmark considered in the adversarial robustness community. It then discusses adversarial examples beyond image classification, other threat models beyond testing time attack, and applications on adversarial robustness. For researchers, this book provides a thorough literature review that summarizes latest progress in this area, which can be a good reference for conducting future research. It could also be used as a textbook for graduate courses on adversarial robustness or trustworthy machine learning. Summarizes the whole field of adversarial robustness for Machine learning models A clearly explained, self-contained reference Introduces formulations, algorithms and intuitions Includes applications based on adversarial robustness

Enhancing Adversarial Robustness of Deep Neural Networks

Enhancing Adversarial Robustness of Deep Neural Networks Book
Author : Jeffrey Zhang (M. Eng.)
Publisher : Unknown
Release : 2019
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Logit-based regularization and pretrain-then-tune are two approaches that have recently been shown to enhance adversarial robustness of machine learning models. In the realm of regularization, Zhang et al. (2019) proposed TRADES, a logit-based regularization optimization function that has been shown to improve upon the robust optimization framework developed by Madry et al. (2018) [14, 9]. They were able to achieve state-of-the-art adversarial accuracy on CIFAR10. In the realm of pretrain- then-tune models, Hendrycks el al. (2019) demonstrated that adversarially pretraining a model on ImageNet then adversarially tuning on CIFAR10 greatly improves the adversarial robustness of machine learning models. In this work, we propose Adversarial Regularization, another logit-based regularization optimization framework that surpasses TRADES in adversarial generalization. Furthermore, we explore the impact of trying different types of adversarial training on the pretrain-then-tune paradigm.

Artificial Neural Networks and Machine Learning ICANN 2021

Artificial Neural Networks and Machine Learning     ICANN 2021 Book
Author : Igor Farkaš,Paolo Masulli,Sebastian Otte,Stefan Wermter
Publisher : Springer Nature
Release : 2021-09-11
ISBN : 303086362X
Language : En, Es, Fr & De

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Book Description :

The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes. In this volume, the papers focus on topics such as adversarial machine learning, anomaly detection, attention and transformers, audio and multimodal applications, bioinformatics and biosignal analysis, capsule networks and cognitive models. *The conference was held online 2021 due to the COVID-19 pandemic.

On the Robustness of Neural Network Attacks and Defenses

On the Robustness of Neural Network  Attacks and Defenses Book
Author : Minhao Cheng
Publisher : Unknown
Release : 2021
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples. That is, a slightly modified example could be easily generated and fool a well-trained image classifier based on deep neural networks (DNNs) with high confidence. This makes it difficult to apply neural networks in security-critical areas. To find such examples, we first introduce and define adversarial examples. In the first part, we then discuss how to build adversarial attacks in both image and discrete domains. For image classification, we introduce how to design an adversarial attacker in three different settings. Among them, we focus on the most practical setup for evaluating the adversarial robustness of a machine learning system with limited access: the hard-label black-box attack setting for generating adversarial examples, where limited model queries are allowed and only the decision is provided to a queried data input. For the discrete domain, we first talk about its difficulty and introduce how to conduct the adversarial attack on two applications. While crafting adversarial examples is an important technique to evaluate the robustness of DNNs, there is a huge need for improving the model robustness as well. Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy machine learning systems. In the second part, we talk about the methods to strengthen the model's adversarial robustness. We first discuss attack-dependent defense. Specifically, we first discuss one of the most effective methods for improving the robustness of neural networks: adversarial training and its limitations. We introduce a variant to overcome its problem. Then we take a different perspective and introduce attack-independent defense. We summarize the current methods and introduce a framework-based vicinal risk minimization. Inspired by the framework, we introduce self-progressing robust training. Furthermore, we discuss the robustness trade-off problem and introduce a hypothesis and propose a new method to alleviate it.

Robust Machine Learning in Adversarial Setting with Provable Guarantee

Robust Machine Learning in Adversarial Setting with Provable Guarantee Book
Author : Yizhen Wang
Publisher : Unknown
Release : 2020
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Over the last decade, machine learning systems have achieved state-of-the-art performance in many fields, and are now used in increasing number of applications. However, recent research work has revealed multiple attacks to machine learning systems that significantly reduce the performance by manipulating the training or test data. As machine learning is increasingly involved in high-stake decision making processes, the robustness of machine learning systems in adversarial environment becomes a major concern. This dissertation attempts to build machine learning systems robust to such adversarial manipulation with the emphasis on providing theoretical performance guarantees. We consider adversaries in both test and training time, and make the following contributions. First, we study the robustness of machine learning algorithms and model to test-time adversarial examples. We analyze the distributional and finite sample robustness of nearest neighbor classification, and propose a modified 1-Nearest-Neighbor classifier that both has theoretical guarantee and empirical improvement in robustness. Second, we examine the robustness of malware detectors to program transformation. We propose novel attacks that evade existing detectors using program transformation, and then show program normalization as a provably robust defense against such transformation. Finally, we investigate data poisoning attacks and defenses for online learning, in which models update and predict over data stream in real-time. We show efficient attacks for general adversarial objectives, analyze the conditions for which filtering based defenses are effective, and provide practical guidance on choosing defense mechanisms and parameters.

Intelligent Systems and Applications

Intelligent Systems and Applications Book
Author : Kohei Arai,Supriya Kapoor,Rahul Bhatia
Publisher : Springer Nature
Release : 2020
ISBN : 3030551873
Language : En, Es, Fr & De

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Book Description :

The book Intelligent Systems and Applications - Proceedings of the 2020 Intelligent Systems Conference is a remarkable collection of chapters covering a wider range of topics in areas of intelligent systems and artificial intelligence and their applications to the real world. The Conference attracted a total of 545 submissions from many academic pioneering researchers, scientists, industrial engineers, students from all around the world. These submissions underwent a double-blind peer review process. Of those 545 submissions, 177 submissions have been selected to be included in these proceedings. As intelligent systems continue to replace and sometimes outperform human intelligence in decision-making processes, they have enabled a larger number of problems to be tackled more effectively.This branching out of computational intelligence in several directions and use of intelligent systems in everyday applications have created the need for such an international conference which serves as a venue to report on up-to-the-minute innovations and developments. This book collects both theory and application based chapters on all aspects of artificial intelligence, from classical to intelligent scope. We hope that readers find the volume interesting and valuable; it provides the state of the art intelligent methods and techniques for solving real world problems along with a vision of the future research.

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases Book
Author : Peggy Cellier,Kurt Driessens
Publisher : Springer Nature
Release : 2020-03-27
ISBN : 3030438236
Language : En, Es, Fr & De

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Book Description :

This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September 2019. The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been accepted for the following workshops: Workshop on Automating Data Science, ADS 2019; Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence and eXplainable Knowledge Discovery in Data Mining, AIMLAI-XKDD 2019; Workshop on Decentralized Machine Learning at the Edge, DMLE 2019; Workshop on Advances in Managing and Mining Large Evolving Graphs, LEG 2019; Workshop on Data and Machine Learning Advances with Multiple Views; Workshop on New Trends in Representation Learning with Knowledge Graphs; Workshop on Data Science for Social Good, SoGood 2019; Workshop on Knowledge Discovery and User Modelling for Smart Cities, UMCIT 2019; Workshop on Data Integration and Applications Workshop, DINA 2019; Workshop on Machine Learning for Cybersecurity, MLCS 2019; Workshop on Sports Analytics: Machine Learning and Data Mining for Sports Analytics, MLSA 2019; Workshop on Categorising Different Types of Online Harassment Languages in Social Media; Workshop on IoT Stream for Data Driven Predictive Maintenance, IoTStream 2019; Workshop on Machine Learning and Music, MML 2019; Workshop on Large-Scale Biomedical Semantic Indexing and Question Answering, BioASQ 2019.

Machine Learning with Provable Robustness Guarantees

Machine Learning with Provable Robustness Guarantees Book
Author : Huan Zhang
Publisher : Unknown
Release : 2020
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Although machine learning has achieved great success in numerous complicated tasks, many machine learning models lack robustness under the presence of adversaries and can be misled by imperceptible adversarial noises. In this dissertation, we first study the robustness verification problem of machine learning, which gives provable guarantees on worst case performance under arbitrarily strong adversaries. We study two popular machine learning models, deep neural networks (DNNs) and ensemble trees, and design efficient and effective algorithms to provably verify the robustness of these models. For neural networks, we develop a linear relaxation based framework, CROWN, where we relax the non-linear units in DNNs using linear bounds, and propagate linear bounds through the network. We generalize CROWN into a linear relaxation based perturbation analysis (LiRPA) algorithm on any computational graphs and general network architectures to handle irregular neural networks used in practice, and released an open source software package, auto_LiRPA, to facilitate the use of LiRPA for researchers in other fields. For tree ensembles, we reduce the robustness verification algorithm to a max-clique finding problem on a specially created graph, which is very efficient compared to existing approaches and can produce high quality lower or upper bounds for the output of a tree ensemble based classifier. After developing our robustness verification algorithms, we utilize them to create a certified adversarial defense for neural networks, where we explicitly optimize the bounds obtained from verification to greatly improve network robustness in a provable manner. Our LiRPA based training method is very efficient: it can scale to large datasets such as downscaled ImageNet and modern computer vision models such as DenseNet. Lastly, we study the robustness of reinforcement learning (RL), which is more challenging than the problem in supervised learning settings. We focus on the robustness of state observations for a RL agent, and develop the state-adversarial Markov decision process (SA-MDP) to characterize the behavior of a RL agent under adversarially perturbed observations. Based on SA-MDP, we develop two orthogonal approaches to improve the robustness of RL: a state-adversarial regularization helping to improve the robustness of function approximators, and alternating training with learned adversaries (ATLA) to mitigate the intrinsic weakness in a policy. Both approaches are evaluated in various simulated environments and they significantly improve the robustness of RL agents under strong adversarial attacks, including a few novel adversarial attacks proposed by us.

Science of Cyber Security

Science of Cyber Security Book
Author : Feng Liu,Jia Xu,Shouhuai Xu,Moti Yung
Publisher : Springer Nature
Release : 2019-12-06
ISBN : 3030346374
Language : En, Es, Fr & De

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Book Description :

This book constitutes the proceedings of the Second International Conference on Science of Cyber Security, SciSec 2019, held in Nanjing, China, in August 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 62 submissions. These papers cover the following subjects: Artificial Intelligence for Cybersecurity, Machine Learning for Cybersecurity, and Mechanisms for Solving Actual Cybersecurity Problems (e.g., Blockchain, Attack and Defense; Encryptions with Cybersecurity Applications).

Robust Machine Learning Models and Their Applications

Robust Machine Learning Models and Their Applications Book
Author : Hongge Chen (Ph. D.)
Publisher : Unknown
Release : 2021
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Recent studies have demonstrated that machine learning models are vulnerable to adversarial perturbations – a small and human-imperceptible input perturbation can easily change the model output completely. This has created serious security threats to many real applications, so it becomes important to formally verify the robustness of machine learning models. This thesis studies the robustness of deep neural networks as well as tree-based models, and considers the applications of robust machine learning models in deep reinforcement learning. We first develop a novel algorithm to learn robust trees. Our method aims to optimize the performance under the worst case perturbation of input features, which leads to a max-min saddle point problem when splitting nodes in trees. We propose efficient tree building algorithms by approximating the inner minimizer in this saddle point problem, and present efficient implementations for classical information gain based trees as well as state-of-the-art tree boosting models such as XGBoost. Experiments show that our method improve the model robustness significantly. We also propose an efficient method to verify the robustness of tree ensembles. We cast the tree ensembles verification problem as a max-clique problem on a multipartite graph. We develop an efficient multi-level verification algorithm that can give tight lower bounds on robustness of decision tree ensembles, while allowing iterative improvement and termination at any-time. On random forest or gradient boosted decision trees models trained on various datasets, our algorithm is up to hundreds of times faster than the previous approach that requires solving a mixed integer linear programming, and is able to give tight robustness verification bounds on large ensembles with hundreds of deep trees. For neural networks, we contribute a number of empirical studies on the practicality and the hardness of adversarial training. We show that even with adversarial defense, a model’s robustness on a test example has a strong correlation with the distance between that example and the manifold of training data embedded by the network. Test examples that are relatively far away from this manifold are more likely to be vulnerable to adversarial attacks. Consequentially, we demonstrate that an adversarial training based defense is vulnerable to a new class of attacks, the “blind-spot attack,” where the input examples reside in low density regions (“blind-spots”) of the empirical distribution of training data but are still on the valid ground-truth data manifold. Finally, we apply neural network robust training methods to deep reinforcement learning (DRL) to train agents that are robust against perturbations on state observations. We propose the state-adversarial Markov decision process (SA-MDP) to study the fundamental properties of this problem, and propose a theoretically principled regularization which can be applied to different DRL algorithms, including deep Q networks (DQN) and proximal policy optimization (PPO). We significantly improve the robustness of agents under strong white box adversarial attacks, including new attacks of our own.

Engineering Dependable and Secure Machine Learning Systems

Engineering Dependable and Secure Machine Learning Systems Book
Author : Onn Shehory,Eitan Farchi,Guy Barash
Publisher : Springer Nature
Release : 2020-11-07
ISBN : 3030621448
Language : En, Es, Fr & De

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Book Description :

This book constitutes the revised selected papers of the Third International Workshop on Engineering Dependable and Secure Machine Learning Systems, EDSMLS 2020, held in New York City, NY, USA, in February 2020. The 7 full papers and 3 short papers were thoroughly reviewed and selected from 16 submissions. The volume presents original research on dependability and quality assurance of ML software systems, adversarial attacks on ML software systems, adversarial ML and software engineering, etc.

Adversarial Machine Learning

Adversarial Machine Learning Book
Author : Yevgeniy Vorobeychik,Murat Kantarcioglu
Publisher : Morgan & Claypool Publishers
Release : 2018-08-08
ISBN : 168173396X
Language : En, Es, Fr & De

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Book Description :

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. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop. The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research. Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings.

Cyber Security Meets Machine Learning

Cyber Security Meets Machine Learning Book
Author : Xiaofeng Chen
Publisher : Springer Nature
Release : 2021-12-03
ISBN : 9813367261
Language : En, Es, Fr & De

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Book Description :

Download Cyber Security Meets Machine Learning book written by Xiaofeng Chen, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases Book
Author : Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera
Publisher : Springer Nature
Release : 2021
ISBN : 3030676617
Language : En, Es, Fr & De

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Book Description :

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track. .

Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies

Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies Book
Author : National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Computer Science and Telecommunications Board,Board on Mathematical Sciences and Analytics,Intelligence Community Studies Board
Publisher : National Academies Press
Release : 2019-08-22
ISBN : 0309496098
Language : En, Es, Fr & De

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Book Description :

The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11â€"12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop.

Computer Vision ECCV 2020 Workshops

Computer Vision     ECCV 2020 Workshops Book
Author : Adrien Bartoli,Andrea Fusiello
Publisher : Springer Nature
Release : 2021-01-09
ISBN : 3030664155
Language : En, Es, Fr & De

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Book Description :

The 5-volume set, comprising the LNCS books 12535 until 12540, constitutes the refereed proceedings of 28 out of the 45 workshops held at the 16th European Conference on Computer Vision, ECCV 2020. The conference was planned to take place in Glasgow, UK, during August 23-28, 2020, but changed to a virtual format due to the COVID-19 pandemic. The 249 full papers, 18 short papers, and 21 further contributions included in the workshop proceedings were carefully reviewed and selected from a total of 467 submissions. The papers deal with diverse computer vision topics. Part I focusses on adversarial robustness in the real world; bioimage computation; egocentric perception, interaction and computing; eye gaze in VR, AR, and in the wild; TASK-CV workshop and VisDA challenge; and bodily expressed emotion understanding.

Strengthening Deep Neural Networks

Strengthening Deep Neural Networks Book
Author : Katy Warr
Publisher : O'Reilly Media
Release : 2019-07-03
ISBN : 149204492X
Language : En, Es, Fr & De

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Book Description :

As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come

Intelligent Technologies and Applications

Intelligent Technologies and Applications Book
Author : Sule Yildirim Yayilgan
Publisher : Springer Nature
Release : 2021
ISBN : 3030717119
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed post-conference proceedings of the Third International Conference on Intelligent Technologies and Applications, INTAP 2020, held in Grimstad, Norway, in September 2020. The 30 revised full papers and 4 revised short papers presented were carefully reviewed and selected from 117 submissions. The papers of this volume are organized in topical sections on image, video processing and analysis; security and IoT; health and AI; deep learning; biometrics; intelligent environments; intrusion and malware detection; and AIRLEAs.

The Alignment Problem Machine Learning and Human Values

The Alignment Problem  Machine Learning and Human Values Book
Author : Brian Christian
Publisher : W. W. Norton & Company
Release : 2020-10-06
ISBN : 039363583X
Language : En, Es, Fr & De

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Book Description :

A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us—and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole—and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands. The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software. In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story. The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful.

Security Privacy and Anonymity in Computation Communication and Storage

Security  Privacy  and Anonymity in Computation  Communication  and Storage Book
Author : Guojun Wang,Bing Chen,Wei Li,Roberto Di Pietro,Xuefeng Yan,Hao Han
Publisher : Springer Nature
Release : 2021-02-04
ISBN : 3030688518
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the 13th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2020, held in Nanjing, China, in December 2020. The 30 full papers were carefully reviewed and selected from 88 submissions. The papers cover many dimensions including security algorithms and architectures, privacy-aware policies, regulations and techniques, anonymous computation and communication, encompassing fundamental theoretical approaches, practical experimental projects, and commercial application systems for computation, communication and storage. SpaCCS 2020 is held jointly with the 11th International Workshop on Trust, Security and Privacy for Big Data (TrustData 2020), the 10th International Symposium on Trust, Security and Privacy for Emerging Applications (TSP 2020), the 9th International Symposium on Security and Privacy on Internet of Things (SPIoT 2020), the 6th International Symposium on Sensor-Cloud Systems (SCS 2020), the 2nd International Workshop on Communication, Computing, Informatics and Security (CCIS 2020), the First International Workshop on Intelligence and Security in Next Generation Networks (ISNGN 2020), the First International Symposium on Emerging Information Security and Applications (EISA 2020).