491 citations · 693 across the 15 of their papers we have counts for
16 papers
Taking off the Rose-Tinted Glasses: A Critical Look at Adversarial ML Through the Lens of Evasion Attacks
Kevin Eykholt, Farhan Ahmed, Pratik Vaishnavi +1
The vulnerability of machine learning models in adversarial scenarios has garnered significant interest in the academic community over the past decade, resulting in a myriad of att…
Accelerating Certified Robustness Training via Knowledge Transfer
Pratik Vaishnavi, Kevin Eykholt, Amir Rahmati
Training deep neural network classifiers that are certifiably robust against adversarial attacks is critical to ensuring the security and reliability of AI-controlled systems. Alth…
Ares: A System-Oriented Wargame Framework for Adversarial ML
Farhan Ahmed, Pratik Vaishnavi, Kevin Eykholt +1
Since the discovery of adversarial attacks against machine learning models nearly a decade ago, research on adversarial machine learning has rapidly evolved into an eternal war bet…
Transferring Adversarial Robustness Through Robust Representation Matching
Pratik Vaishnavi, Kevin Eykholt, Amir Rahmati
With the widespread use of machine learning, concerns over its security and reliability have become prevalent. As such, many have developed defenses to harden neural networks again…
Can Attention Masks Improve Adversarial Robustness?
Pratik Vaishnavi, Tianji Cong, Kevin Eykholt +2
Deep Neural Networks (DNNs) are known to be susceptible to adversarial examples. Adversarial examples are maliciously crafted inputs that are designed to fool a model, but appear n…
New Problems and Solutions in IoT Security and Privacy
Earlence Fernandes, Amir Rahmati, Nick Feamster
In a previous article for S&P magazine, we made a case for the new intellectual challenges in the Internet of Things security research. In this article, we revisit our earlier obse…
Decentralized Cooperative Communication-less Multi-Agent Task Assignment with Monte-Carlo Tree Search
Mohammadreza Daneshvaramoli, Mohammad Sina Kiarostami, Saleh Khalaj Monfared +5
Cooperative task assignment is an important subject in multi-agent systems with a wide range of applications. These systems are usually designed with massive communication among th…
Towards Model-Agnostic Adversarial Defenses using Adversarially Trained Autoencoders
Pratik Vaishnavi, Kevin Eykholt, Atul Prakash +1
Adversarial machine learning is a well-studied field of research where an adversary causes predictable errors in a machine learning algorithm through precise manipulation of the in…
VISCR: Intuitive & Conflict-free Automation for Securing the Dynamic Consumer IoT Infrastructures
Vasudevan Nagendra, Arani Bhattacharya, Vinod Yegneswaran +2
Consumer IoT is characterized by heterogeneous devices with diverse functionality and programming interfaces. This lack of homogeneity makes the integration and security management…
Physical Adversarial Examples for Object Detectors
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes +6
Deep neural networks (DNNs) are vulnerable to adversarial examples-maliciously crafted inputs that cause DNNs to make incorrect predictions. Recent work has shown that these attack…
Tyche: Risk-Based Permissions for Smart Home Platforms
Amir Rahmati, Earlence Fernandes, Kevin Eykholt +1
Emerging smart home platforms, which interface with a variety of physical devices and support third-party application development, currently use permission models inspired by smart…
Note on Attacking Object Detectors with Adversarial Stickers
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes +6
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 known to be vulnerable…
IFTTT vs. Zapier: A Comparative Study of Trigger-Action Programming Frameworks
Amir Rahmati, Earlence Fernandes, Jaeyeon Jung +1
The growing popularity of online services and IoT platforms along with increased developer's access to devices and services through RESTful APIs is giving rise to a new class of fr…
Decoupled-IFTTT: Constraining Privilege in Trigger-Action Platforms for the Internet of Things
Earlence Fernandes, Amir Rahmati, Jaeyeon Jung +1
Trigger-Action platforms are an emerging class of web-based systems that enable users to create automation rules (or recipes) of the form, "If there is a smoke alarm, then turn off…
Robust Physical-World Attacks on Deep Learning Models
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes +6
Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to the input. G…
Internet of Things Security Research: A Rehash of Old Ideas or New Intellectual Challenges?
Earlence Fernandes, Amir Rahmati, Kevin Eykholt +1
The Internet of Things (IoT) is a new computing paradigm that spans wearable devices, homes, hospitals, cities, transportation, and critical infrastructure. Building security into…