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Showing 1 to 20 of 26 for “"Adversarial machine learning"”.
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No Time to Spare: Adversarial Machine Learning at Training and Inference Time
Contains fulltext : 326242.pdf (Publisher’s version ) (Open Access)
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Adversarial machine learning in computer vision: attacks and defenses on machine learning models
Machine learning models, including neural networks, have gained great popularity in recent years. Deep neural networks are able to directly learn from raw data and can outperform traditional machine learning models. As a result, they have been increasingly used in a variety of application domains …
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DYNAMIC DEFENSES AND THE TRANSFERABILITY OF ADVERSARIAL EXAMPLES
Adversarial machine learning has been an important area of study for the securing of machine learning systems. However, for every defense that is made to protect these artificial learners, a more sophisticated attack emerges to defeat it. This has created an arms race, with the problem of …
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Adversarial attacks and defenses for generative models
Adversarial Machine learning is a field of research lying at the intersection of Machine Learning and Security, which studies vulnerabilities of Machine learning models that make them susceptible to attacks. The attacks are inflicted by carefully designing a perturbed input which appears benign, …
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Rational Multiparty Computation
… We also give an application of game theory to adversarial interactions where cryptography is not necessary. Specifically, we consider adversarial machine learning, where the adversary is rational and reacts to the presence of a data miner. We give a general extension to classification …
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Threat-Analysis Oriented Digital Twinning of ML-Powered Future Autonomous Weapon Systems
… integration of artificial intelligence (AI) and machine learning (ML) into autonomous weapon systems (AWS) for perception, decision support, and control. As these systems become more software-defined, their cyber attack surface expands across sensing, communications, autonomy logic, and …
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Adversarial-resilience assurance for mobile security systems
… techniques, such as program analysis and machine learning, have been introduced in the mobile security systems for better security decision making. However, these intelligent techniques are originally proposed for domains such as image recognition, Virtual Personal Assistants, and software …
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Bias and Fairness of Evasion Attacks in Image Perturbation
… about protecting privacy of personal images, adversarial attack methods play key roles. These methods are created to protect against the unauthorized usage of personal images. Such methods protect personal privacy by adding some amount of perturbations, otherwise known as "noise", to input …
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Building trustworthy machine learning systems in adversarial environments
… particularly with the rise of big data and deep learning in the last decade, have greatly improved our daily life and at the same time created a long list of controversies. AI systems are often subject to malicious and stealthy subversion that jeopardizes their efficacy. Many of these issues stem …
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Person Re-identification and an Adversarial Attack and Defense for Person Re-identification Networks
… improved significantly. However, latest works in adversarial machine learning have shown the vulnerabilities of DNNs against adversarial examples, which are carefully crafted images that are similar to original/benign images, but can deceive the neural network models. Neural network-based ReID …
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Person Re-identification And An Adversarial Attack And Defense For Person Re-identification Networks
… improved significantly. However, latest works in adversarial machine learning have shown the vulnerabilities of DNNs against adversarial examples, which are carefully crafted images that are similar to original/benign images, but can deceive the neural network models. Neural network-based ReID …
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Certified robustness of modern machine learning methods
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01
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A defensive strategy for detecting targeted adversarial poisoning attacks in machine learning trained malware detection models
Machine learning is a subset of Artificial Intelligence which is utilised in a variety of different fields to increase productivity, reduce overheads, and simplify the work process through training machines to automatically perform a task. Machine learning has been implemented in many different …
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Enhancing Communications Aware Evasion Attacks on RFML Spectrum Sensing Systems
Recent innovations in machine learning have paved the way for new capabilities in the field of radio frequency (RF) communications. Machine learning techniques such as reinforcement learning and deep neural networks (DNN) can be leveraged to improve upon traditional wireless communications methods …
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A mixed-method approach to analyze the robustness of natural language processing classifiers
… A large challenge with evaluating these adversarial attacks is the trade-o_ between attack efficiency and text quality. Higher constraints on the attack search space will improve text quality but reduce the attack success rate. In this thesis, I introduce a framework for the evaluation of …
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Secure Machine Learning Based RF Signal Classification for Wireless Systems
… these classifiers are, in general, vulnerable to adversarial machine learning (AML) attacks. In one type of AML attack, the adversary trains a surrogate classifier (called the {\em attacker's classifier}) to produce intelligently crafted low-power ``perturbations'' that degrade the accuracy of the …
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HUMAN ACTIVITY RECOGNITION FROM EGOCENTRIC VIDEOS AND ROBUSTNESS ANALYSIS OF DEEP NEURAL NETWORKS
… of the activities that can be detected. Deep machine learning has achieved great success in image and video processing in recent years. Neural network based models provide improved accuracy in multiple fields in computer vision. However, there has been relatively less work focusing on …
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AI-infused security: Robust defense by bridging theory and practice
… three interrelated research thrusts. (1) Adversarial Attack and Defense of Deep Neural Networks: We discover vulnerabilities of deep neural networks in real-world settings and the countermeasures to mitigate the threat. We develop ShapeShifter, the first targeted physical adversarial …
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Deception and defense from machine learning to supply chains
… a core building block of modern systems, we can adversarially manipulate dependent applications ranging from natural language processing pipelines to search engines to code compilers. Left undefended, these vulnerabilities enable many ill effects including uncurtailed online hate speech, …
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