Search Results - "Eykholt, Kevin"

Robust Physical-World Attacks on Deep Learning Visual Classification by Eykholt, Kevin, Evtimov, Ivan, Fernandes, Earlence, Li, Bo, Rahmati, Amir, Xiao, Chaowei, Prakash, Atul, Kohno, Tadayoshi, Song, Dawn

“…Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations…”
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Designing and Evaluating Physical Adversarial Attacks and Defenses for Machine Learning Algorithms by Eykholt, Kevin

“…Studies show that state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to…”
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Taking off the Rose-Tinted Glasses: A Critical Look at Adversarial ML Through the Lens of Evasion Attacks by Eykholt, Kevin, Ahmed, Farhan, Vaishnavi, Pratik, Rahmati, Amir

“…The vulnerability of machine learning models in adversarial scenarios has garnered significant interest in the academic community over the past decade,…”
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Accelerating Certified Robustness Training via Knowledge Transfer by Vaishnavi, Pratik, Eykholt, Kevin, Rahmati, Amir

“…Training deep neural network classifiers that are certifiably robust against adversarial attacks is critical to ensuring the security and reliability of…”
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Tyche: A Risk-Based Permission Model for Smart Homes by Rahmati, Amir, Fernandes, Earlence, Eykholt, Kevin, Prakash, Atul

“…Emerging smart home platforms, which interface with a variety of physical devices and support third-party application development, currently use permission…”
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Transferring Adversarial Robustness Through Robust Representation Matching by Vaishnavi, Pratik, Eykholt, Kevin, Rahmati, Amir

“…With the widespread use of machine learning, concerns over its security and reliability have become prevalent. As such, many have developed defenses to harden…”
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Designing Adversarially Resilient Classifiers using Resilient Feature Engineering by Eykholt, Kevin, Prakash, Atul

“…We provide a methodology, resilient feature engineering, for creating adversarially resilient classifiers. According to existing work, adversarial attacks…”
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Ares: A System-Oriented Wargame Framework for Adversarial ML by Ahmed, Farhan, Vaishnavi, Pratik, Eykholt, Kevin, Rahmati, Amir

“…Since the discovery of adversarial attacks against machine learning models nearly a decade ago, research on adversarial machine learning has rapidly evolved…”
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Ares: A System-Oriented Wargame Framework for Adversarial ML by Ahmed, Farhan, Vaishnavi, Pratik, Eykholt, Kevin, Rahmati, Amir

“…Since the discovery of adversarial attacks against machine learning models nearly a decade ago, research on adversarial machine learning has rapidly evolved…”
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URET: Universal Robustness Evaluation Toolkit (for Evasion) by Eykholt, Kevin, Lee, Taesung, Schales, Douglas, Jang, Jiyong, Molloy, Ian, Zorin, Masha

“…Machine learning models are known to be vulnerable to adversarial evasion attacks as illustrated by image classification models. Thoroughly understanding such…”
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Adaptive Verifiable Training Using Pairwise Class Similarity by Wang, Shiqi, Eykholt, Kevin, Lee, Taesung, Jang, Jiyong, Molloy, Ian

“…Verifiable training has shown success in creating neural networks that are provably robust to a given amount of noise. However, despite only enforcing a single…”
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Benchmarking the Effect of Poisoning Defenses on the Security and Bias of Deep Learning Models by Baracaldo, Nathalie, Ahmed, Farhan, Eykholt, Kevin, Zhou, Yi, Priya, Shriti, Lee, Taesung, Kadhe, Swanand, Tan, Mike, Polavaram, Sridevi, Suggs, Sterling, Gao, Yuyang, Slater, David

“…Machine learning models are susceptible to a class of attacks known as adversarial poisoning where an adversary can maliciously manipulate training data to…”
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Can Attention Masks Improve Adversarial Robustness? by Vaishnavi, Pratik, Cong, Tianji, Eykholt, Kevin, Prakash, Atul, Rahmati, Amir

“…Deep Neural Networks (DNNs) are known to be susceptible to adversarial examples. Adversarial examples are maliciously crafted inputs that are designed to fool…”
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Towards Model-Agnostic Adversarial Defenses using Adversarially Trained Autoencoders by Vaishnavi, Pratik, Eykholt, Kevin, Prakash, Atul, Rahmati, Amir

“…Adversarial machine learning is a well-studied field of research where an adversary causes predictable errors in a machine learning algorithm through precise…”
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Separation of Powers in Federated Learning by Cheng, Pau-Chen, Eykholt, Kevin, Gu, Zhongshu, Jamjoom, Hani, Jayaram, K. R, Valdez, Enriquillo, Verma, Ashish

“…Federated Learning (FL) enables collaborative training among mutually distrusting parties. Model updates, rather than training data, are concentrated and fused…”
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Tyche: Risk-Based Permissions for Smart Home Platforms by Rahmati, Amir, Fernandes, Earlence, Eykholt, Kevin, Prakash, Atul

“…Emerging smart home platforms, which interface with a variety of physical devices and support third-party application development, currently use permission…”
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Robust Classification using Robust Feature Augmentation by Eykholt, Kevin, Gupta, Swati, Prakash, Atul, Rahmati, Amir, Vaishnavi, Pratik, Zheng, Haizhong

“…Existing deep neural networks, say for image classification, have been shown to be vulnerable to adversarial images that can cause a DNN misclassification,…”
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Internet of Things Security Research: A Rehash of Old Ideas or New Intellectual Challenges? by Fernandes, Earlence, Rahmati, Amir, Eykholt, Kevin, Prakash, Atul

“…The Internet of Things (IoT) is a new computing paradigm that spans wearable devices, homes, hospitals, cities, transportation, and critical infrastructure…”
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Physical Adversarial Examples for Object Detectors by Eykholt, Kevin, Evtimov, Ivan, Fernandes, Earlence, Li, Bo, Rahmati, Amir, Tramer, Florian, Prakash, Atul, Kohno, Tadayoshi, Song, Dawn

“…Deep neural networks (DNNs) are vulnerable to adversarial examples-maliciously crafted inputs that cause DNNs to make incorrect predictions. Recent work has…”
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Note on Attacking Object Detectors with Adversarial Stickers by Eykholt, Kevin, Evtimov, Ivan, Fernandes, Earlence, Li, Bo, Song, Dawn, Kohno, Tadayoshi, Rahmati, Amir, Prakash, Atul, Tramer, Florian

“…Deep learning has proven to be a powerful tool for computer vision and has seen widespread adoption for numerous tasks. However, deep learning algorithms are…”
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