37 citations · 106 across the 13 of their papers we have counts for
14 papers
Understanding and Diagnosing Vulnerability under Adversarial Attacks
Haizhong Zheng, Ziqi Zhang, Honglak Lee +1
Deep Neural Networks (DNNs) are known to be vulnerable to adversarial attacks. Currently, there is no clear insight into how slight perturbations cause such a large difference in c…
Towards Robustness against Unsuspicious Adversarial Examples
Liang Tong, Minzhe Guo, Atul Prakash +1
Despite the remarkable success of deep neural networks, significant concerns have emerged about their robustness to adversarial perturbations to inputs. While most attacks aim to e…
MAZE: Data-Free Model Stealing Attack Using Zeroth-Order Gradient Estimation
Sanjay Kariyappa, Atul Prakash, Moinuddin Qureshi
Model Stealing (MS) attacks allow an adversary with black-box access to a Machine Learning model to replicate its functionality, compromising the confidentiality of the model. Such…
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…
Efficient Adversarial Training with Transferable Adversarial Examples
Haizhong Zheng, Ziqi Zhang, Juncheng Gu +2
Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can require or…
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…
Analyzing the Interpretability Robustness of Self-Explaining Models
Haizhong Zheng, Earlence Fernandes, Atul Prakash
Recently, interpretable models called self-explaining models (SEMs) have been proposed with the goal of providing interpretability robustness. We evaluate the interpretability robu…
Beware of the App! On the Vulnerability Surface of Smart Devices through their Companion Apps
Davino Mauro Junior, Luis Melo, Harvey Lu +2
Internet of Things (IoT) devices are becoming increasingly important. These devices are often resource-limited, hindering rigorous enforcement of security policies. Assessing the v…
Designing Adversarially Resilient Classifiers using Resilient Feature Engineering
Kevin Eykholt, Atul Prakash
We provide a methodology, resilient feature engineering, for creating adversarially resilient classifiers. According to existing work, adversarial attacks identify weakly correlate…
Securing IoT Apps with Fine-grained Control of Information Flows
Davino Mauro Junior, Kiev Gama, Atul Prakash
Internet of Things is growing rapidly, with many connected devices now available to consumers. With this growth, the IoT apps that manage the devices from smartphones raise signifi…
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…
Anception: Application Virtualization For Android
Earlence Fernandes, Alexander Crowell, Ajit Aluri +1
The problem of malware has become significant on Android devices. Library operating systems and application virtualization are both possible solutions for confining malware. Unfort…