Author: William Lawrence
Publisher: Independently Published
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Dive into the cutting-edge realm of adversarial attacks and defenses with acclaimed author William J. Lawrence in his groundbreaking book, "Adversarial Frontiers: Exploring FGSM and PGD." As our digital landscapes become increasingly complex, Lawrence demystifies the world of Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD), unraveling the intricacies of these adversarial techniques that have the potential to reshape cybersecurity. In this meticulously researched and accessible guide, Lawrence takes readers on a journey through the dynamic landscapes of machine learning and artificial intelligence, offering a comprehensive understanding of how adversarial attacks exploit vulnerabilities in these systems. With a keen eye for detail, he explores the nuances of FGSM and PGD, shedding light on their inner workings and the potential threats they pose to our interconnected world. But Lawrence doesn't stop at exposing vulnerabilities; he empowers readers with invaluable insights into state-of-the-art defense mechanisms. Drawing on his expertise in the field, Lawrence equips both novice and seasoned cybersecurity professionals with the knowledge and tools needed to fortify systems against adversarial intrusions. Through real-world examples and practical applications, he demonstrates the importance of robust defense strategies in safeguarding against the evolving landscape of cyber threats. "Adversarial Frontiers" stands as a beacon of clarity in the often murky waters of adversarial attacks. William J. Lawrence's articulate prose and engaging narrative make this book a must-read for anyone seeking to navigate the complexities of FGSM and PGD. Whether you're an aspiring data scientist, a seasoned cybersecurity professional, or a curious mind eager to understand the digital battlegrounds of tomorrow, Lawrence's work provides the essential roadmap for comprehending and mitigating adversarial risks in the age of artificial intelligence.
Author: Aqib Rashid
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Joseph Schuessler
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
This work explores adversarial imperceptible attacks on time series data in recurrent neural networks to learn both security of deep recurrent neural networks and to understand properties of learning in deep recurrent neural networks. Because deep neural networks are widely used in application areas, there exists the possibility to degrade the accuracy and security by adversarial methods. The adversarial method explored in this work is backdoor data poisoning where an adversary poisons training samples with a small perturbation to misclassify a source class to a target class. In backdoor poisoning, the adversary has access to a subset of training data, with labels, the ability to poison the training samples, and the ability to change the source class s* label to the target class t* label. The adversary does not have access to the classifier during the training or knowledge of the training process. This work also explores post training defense of backdoor data poisoning by reviewing an iterative method to determine the source and target class pair in such an attack. The backdoor poisoning methods introduced in this work successfully fool a LSTM classifier without degrading the accuracy of test samples without the backdoor pattern present. Second, the defense method successfully determines the source class pair in such an attack. Third, backdoor poisoning in LSTMs require either more training samples or a larger perturbation than a standard feedforward network. LSTM also require larger hidden units and more iterations for a successful attack. Last, in the defense of LSTMs, the gradient based method produces larger gradients towards the tail end of the time series indicating an interesting property of LSTMS in which most of learning occurs in the memory of LSTM nodes.
Author: Jinyin Chen
Publisher: Springer Nature
ISBN: 9819704251
Category :
Languages : en
Pages : 413
Get Book Here
Book Description
Author: Adrien Bartoli
Publisher: Springer Nature
ISBN: 3030668231
Category : Computers
Languages : en
Pages : 777
Get Book Here
Book Description
The 6-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 IV focusses on advances in image manipulation; assistive computer vision and robotics; and computer vision for UAVs.
Author: Aneesh Sreevallabh Chivukula
Publisher: Springer Nature
ISBN: 3030997723
Category : Computers
Languages : en
Pages : 316
Get Book Here
Book Description
A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.
Author: Pushpendu Kar
Publisher: Springer Nature
ISBN: 946463300X
Category : Computers
Languages : en
Pages : 1077
Get Book Here
Book Description
This is an open access book. Scope of Conference 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI2023), which will be held from August 11 to August 13 in Singapore provides a forum for researchers and experts in different but related fields to discuss research findings. The scope of ICIAAI 2023 covers research areas such as imaging, algorithms and artificial intelligence. Related fields of research include computer software, programming languages, software engineering, computer science applications, artificial intelligence, Intelligent data analysis, deep learning, high-performance computing, signal processing, information systems, computer graphics, computer-aided design, Computer vision, etc. The objectives of the conference are: The conference aims to provide a platform for experts, scholars, engineers and technicians engaged in the research of image, algorithm and artificial intelligence to share scientific research results and cutting-edge technologies. The conference will discuss the academic trends and development trends of the related research fields of image, algorithm and artificial intelligence together, carry out discussions on current hot issues, and broaden research ideas. It will be a perfect gathering to strengthen academic research and discussion, promote the development and progress of relevant research and application, and promote the development of disciplines and promote talent training.
Author: Danail Stoyanov
Publisher: Springer
ISBN: 3030026280
Category : Computers
Languages : en
Pages : 149
Get Book Here
Book Description
This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis.
Author: John Sotiropoulos
Publisher: Packt Publishing Ltd
ISBN: 1835088678
Category : Computers
Languages : en
Pages : 586
Get Book Here
Book Description
Understand how adversarial attacks work against predictive and generative AI, and learn how to safeguard AI and LLM projects with practical examples leveraging OWASP, MITRE, and NIST Key Features Understand the connection between AI and security by learning about adversarial AI attacks Discover the latest security challenges in adversarial AI by examining GenAI, deepfakes, and LLMs Implement secure-by-design methods and threat modeling, using standards and MLSecOps to safeguard AI systems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionAdversarial attacks trick AI systems with malicious data, creating new security risks by exploiting how AI learns. This challenges cybersecurity as it forces us to defend against a whole new kind of threat. This book demystifies adversarial attacks and equips cybersecurity professionals with the skills to secure AI technologies, moving beyond research hype or business-as-usual strategies. The strategy-based book is a comprehensive guide to AI security, presenting a structured approach with practical examples to identify and counter adversarial attacks. This book goes beyond a random selection of threats and consolidates recent research and industry standards, incorporating taxonomies from MITRE, NIST, and OWASP. Next, a dedicated section introduces a secure-by-design AI strategy with threat modeling to demonstrate risk-based defenses and strategies, focusing on integrating MLSecOps and LLMOps into security systems. To gain deeper insights, you’ll cover examples of incorporating CI, MLOps, and security controls, including open-access LLMs and ML SBOMs. Based on the classic NIST pillars, the book provides a blueprint for maturing enterprise AI security, discussing the role of AI security in safety and ethics as part of Trustworthy AI. By the end of this book, you’ll be able to develop, deploy, and secure AI systems effectively.What you will learn Understand poisoning, evasion, and privacy attacks and how to mitigate them Discover how GANs can be used for attacks and deepfakes Explore how LLMs change security, prompt injections, and data exposure Master techniques to poison LLMs with RAG, embeddings, and fine-tuning Explore supply-chain threats and the challenges of open-access LLMs Implement MLSecOps with CIs, MLOps, and SBOMs Who this book is for This book tackles AI security from both angles - offense and defense. AI builders (developers and engineers) will learn how to create secure systems, while cybersecurity professionals, such as security architects, analysts, engineers, ethical hackers, penetration testers, and incident responders will discover methods to combat threats and mitigate risks posed by attackers. The book also provides a secure-by-design approach for leaders to build AI with security in mind. To get the most out of this book, you’ll need a basic understanding of security, ML concepts, and Python.
Author: Linwei Wang
Publisher: Springer Nature
ISBN: 3031164520
Category : Computers
Languages : en
Pages : 774
Get Book Here
Book Description
The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization; Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.
