Backdoor Attacks Against Learning Based Algorithms

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Backdoor Attacks Against Learning Based Algorithms

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Author : Shaofeng Li
language : en
Publisher: Springer Nature
Release Date : 2024-05-29

Backdoor Attacks Against Learning Based Algorithms written by Shaofeng Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-29 with Computers categories.

This book introduces a new type of data poisoning attack, dubbed, backdoor attack. In backdoor attacks, an attacker can train the model with poisoned data to obtain a model that performs well on a normal input but behaves wrongly with crafted triggers. Backdoor attacks can occur in many scenarios where the training process is not entirely controlled, such as using third-party datasets, third-party platforms for training, or directly calling models provided by third parties. Due to the enormous threat that backdoor attacks pose to model supply chain security, they have received widespread attention from academia and industry. This book focuses on exploiting backdoor attacks in the three types of DNN applications, which are image classification, natural language processing, and federated learning. Based on the observation that DNN models are vulnerable to small perturbations, this book demonstrates that steganography and regularization can be adopted to enhance the invisibility of backdoor triggers. Based on image similarity measurement, this book presents two metrics to quantitatively measure the invisibility of backdoor triggers. The invisible trigger design scheme introduced in this book achieves a balance between the invisibility and the effectiveness of backdoor attacks. In the natural language processing domain, it is difficult to design and insert a general backdoor in a manner imperceptible to humans. Any corruption to the textual data (e.g., misspelled words or randomly inserted trigger words/sentences) must retain context-awareness and readability to human inspectors. This book introduces two novel hidden backdoor attacks, targeting three major natural language processing tasks, including toxic comment detection, neural machine translation, and question answering, depending on whether the targeted NLP platform accepts raw Unicode characters. The emerged distributed training framework, i.e., federated learning, has advantages in preserving users' privacy. It has been widely used in electronic medical applications, however, it also faced threats derived from backdoor attacks. This book presents a novel backdoor detection framework in FL-based e-Health systems. We hope this book can provide insightful lights on understanding the backdoor attacks in different types of learning-based algorithms, including computer vision, natural language processing, and federated learning. The systematic principle in this book also offers valuable guidance on the defense of backdoor attacks against future learning-based algorithms.

Cryptology And Network Security

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Author : Stephan Krenn
language : en
Publisher: Springer Nature
Release Date : 2020-12-09

Cryptology And Network Security written by Stephan Krenn and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-09 with Computers categories.

This book constitutes the refereed proceedings of the 19th International Conference on Cryptology and Network Security, CANS 2020, held in Vienna, Austria, in December 2020.* The 30 full papers were carefully reviewed and selected from 118 submissions. The papers focus on topics such as cybersecurity; credentials; elliptic curves; payment systems; privacy-enhancing tools; lightweight cryptography; and codes and lattices. *The conference was held virtually due to the COVID-19 pandemic.

Machine Learning For Cyber Security

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Author : Yang Xiang
language : en
Publisher: Springer Nature
Release Date : 2025-05-02

Machine Learning For Cyber Security written by Yang Xiang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-02 with Computers categories.

This book constitutes the referred proceedings of the 6th International Conference on Machine Learning for Cyber Security, ML4CS 2024, held in Hangzhou, China, during December 27–29, 2024. The 30 full papers presented in this book were carefully reviewed and selected from 111 submissions. ML4CS is a well-recognized annual international forum for AI-driven security researchers to exchange ideas and present their works. The conference focus on topics such as blockchain, network security, system security, software security, threat intelligence, cybersecurity situational awareness and much many more.

Embedded Machine Learning For Cyber Physical Iot And Edge Computing

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Author : Sudeep Pasricha
language : en
Publisher: Springer Nature
Release Date : 2023-10-06

Embedded Machine Learning For Cyber Physical Iot And Edge Computing written by Sudeep Pasricha and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-06 with Technology & Engineering categories.

This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.

Handbook Of Trustworthy Federated Learning

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Author : My T. Thai
language : en
Publisher: Springer Nature
Release Date : 2024-09-03

Handbook Of Trustworthy Federated Learning written by My T. Thai and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-03 with Computers categories.

This handbook aims to serve as a one-stop, reliable resource, including curated surveys and expository contributions on federated learning. It covers a comprehensive range of topics, providing the reader with technical and non-technical fundamentals, applications, and extensive details of various topics. The readership spans from researchers and academics to practitioners who are deeply engaged or are starting to venture into the realms of trustworthy federated learning. First introduced in 2016, federated learning allows devices to collaboratively learn a shared model while keeping raw data localized, thus promising to protect data privacy. Since its introduction, federated learning has undergone several evolutions. Most importantly, its evolution is in response to the growing recognition that its promise of collaborative learning is inseparable from the imperatives of privacy preservation and model security. The resource is divided into four parts. Part 1 (Security and Privacy) explores the robust defense mechanisms against targeted attacks and addresses fairness concerns, providing a multifaceted foundation for securing Federated Learning systems against evolving threats. Part 2 (Bilevel Optimization) unravels the intricacies of optimizing performance in federated settings. Part 3 (Graph and Large Language Models) addresses the challenges in training Graph Neural Networks and ensuring privacy in Federated Learning of natural language models. Part 4 (Edge Intelligence and Applications) demonstrates how Federated Learning can empower mobile applications and preserve privacy with synthetic data.

Algorithms And Architectures For Parallel Processing

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Author : Zahir Tari
language : en
Publisher: Springer Nature
Release Date : 2024-02-26

Algorithms And Architectures For Parallel Processing written by Zahir Tari and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-26 with Computers categories.

The 7-volume set LNCS 14487-14493 constitutes the proceedings of the 23rd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2023, which took place in Tianjin, China, during October, 2023. The 145 full papers included in this book were carefully reviewed and selected from 439 submissions. ICA3PP covers the many dimensions of parallel algorithms and architectures; encompassing fundamental theoretical approaches; practical experimental projects; and commercial components and systems.

Security And Artificial Intelligence

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Author : Lejla Batina
language : en
Publisher: Springer Nature
Release Date : 2022-04-07

Security And Artificial Intelligence written by Lejla Batina and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-07 with Computers categories.

AI has become an emerging technology to assess security and privacy, with many challenges and potential solutions at the algorithm, architecture, and implementation levels. So far, research on AI and security has looked at subproblems in isolation but future solutions will require sharing of experience and best practice in these domains. The editors of this State-of-the-Art Survey invited a cross-disciplinary team of researchers to a Lorentz workshop in 2019 to improve collaboration in these areas. Some contributions were initiated at the event, others were developed since through further invitations, editing, and cross-reviewing. This contributed book contains 14 invited chapters that address side-channel attacks and fault injection, cryptographic primitives, adversarial machine learning, and intrusion detection. The chapters were evaluated based on their significance, technical quality, and relevance to the topics of security and AI, and each submission was reviewed in single-blind mode and revised.

Quantum Safe Cryptography Algorithms And Approaches

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Author : Satya Prakash Yadav
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2023-08-07

Quantum Safe Cryptography Algorithms And Approaches written by Satya Prakash Yadav and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-07 with Computers categories.

Quantum computers have demonstrated that they have the inherent potential to outperform classical computers in many areas. One of the major impacts is that the currently available cryptography algorithms are bound to no longer hold once quantum computers are able to compute at full speed. This book presents an overview of all the cross-disciplinary developments in cybersecurity that are being generated by the advancements in quantum computing.

Digital Watermarking For Machine Learning Model

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Author : Lixin Fan
language : en
Publisher: Springer Nature
Release Date : 2023-05-29

Digital Watermarking For Machine Learning Model written by Lixin Fan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-29 with Computers categories.

Machine learning (ML) models, especially large pretrained deep learning (DL) models, are of high economic value and must be properly protected with regard to intellectual property rights (IPR). Model watermarking methods are proposed to embed watermarks into the target model, so that, in the event it is stolen, the model’s owner can extract the pre-defined watermarks to assert ownership. Model watermarking methods adopt frequently used techniques like backdoor training, multi-task learning, decision boundary analysis etc. to generate secret conditions that constitute model watermarks or fingerprints only known to model owners. These methods have little or no effect on model performance, which makes them applicable to a wide variety of contexts. In terms of robustness, embedded watermarks must be robustly detectable against varying adversarial attacks that attempt to remove the watermarks. The efficacy of model watermarking methods is showcased in diverse applications including image classification, image generation, image captions, natural language processing and reinforcement learning. This book covers the motivations, fundamentals, techniques and protocols for protecting ML models using watermarking. Furthermore, it showcases cutting-edge work in e.g. model watermarking, signature and passport embedding and their use cases in distributed federated learning settings.

Four Battlegrounds Power In The Age Of Artificial Intelligence

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Author : Paul Scharre
language : en
Publisher: W. W. Norton & Company
Release Date : 2023-02-28

Four Battlegrounds Power In The Age Of Artificial Intelligence written by Paul Scharre and has been published by W. W. Norton & Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-28 with History categories.

An NPR 2023 "Books We Love" Pick One of the Next Big Idea Club's Must-Read Books "An invaluable primer to arguably the most important driver of change for our future." —P. W. Singer, author of Burn-In An award-winning defense expert tells the story of today’s great power rivalry—the struggle to control artificial intelligence. A new industrial revolution has begun. Like mechanization or electricity before it, artificial intelligence will touch every aspect of our lives—and cause profound disruptions in the balance of global power, especially among the AI superpowers: China, the United States, and Europe. Autonomous weapons expert Paul Scharre takes readers inside the fierce competition to develop and implement this game-changing technology and dominate the future. Four Battlegrounds argues that four key elements define this struggle: data, computing power, talent, and institutions. Data is a vital resource like coal or oil, but it must be collected and refined. Advanced computer chips are the essence of computing power—control over chip supply chains grants leverage over rivals. Talent is about people: which country attracts the best researchers and most advanced technology companies? The fourth “battlefield” is maybe the most critical: the ultimate global leader in AI will have institutions that effectively incorporate AI into their economy, society, and especially their military. Scharre’s account surges with futuristic technology. He explores the ways AI systems are already discovering new strategies via millions of war-game simulations, developing combat tactics better than any human, tracking billions of people using biometrics, and subtly controlling information with secret algorithms. He visits China’s “National Team” of leading AI companies to show the chilling synergy between China’s government, private sector, and surveillance state. He interviews Pentagon leadership and tours U.S. Defense Department offices in Silicon Valley, revealing deep tensions between the military and tech giants who control data, chips, and talent. Yet he concludes that those tensions, inherent to our democratic system, create resilience and resistance to autocracy in the face of overwhelmingly powerful technology. Engaging and direct, Four Battlegrounds offers a vivid picture of how AI is transforming warfare, global security, and the future of human freedom—and what it will take for democracies to remain at the forefront of the world order.