Search Results for author: Earlence Fernandes

Found 17 papers, 6 papers with code

SkillFence: A Systems Approach to Practically Mitigating Voice-Based Confusion Attacks

no code implementations16 Dec 2022 Ashish Hooda, Matthew Wallace, Kushal Jhunjhunwalla, Earlence Fernandes, Kassem Fawaz

Our key insight is that we can interpret a user's intentions by analyzing their activity on counterpart systems of the web and smartphones.

Re-purposing Perceptual Hashing based Client Side Scanning for Physical Surveillance

no code implementations8 Dec 2022 Ashish Hooda, Andrey Labunets, Tadayoshi Kohno, Earlence Fernandes

Content scanning systems employ perceptual hashing algorithms to scan user content for illegal material, such as child pornography or terrorist recruitment flyers.

Exploring Adversarial Robustness of Deep Metric Learning

1 code implementation14 Feb 2021 Thomas Kobber Panum, Zi Wang, Pengyu Kan, Earlence Fernandes, Somesh Jha

Deep Metric Learning (DML), a widely-used technique, involves learning a distance metric between pairs of samples.

Adversarial Robustness Metric Learning

Adversarial Deep Metric Learning

no code implementations1 Jan 2021 Thomas Kobber Panum, Zi Wang, Pengyu Kan, Earlence Fernandes, Somesh Jha

To the best of our knowledge, we are the first to systematically analyze this dependence effect and propose a principled approach for robust training of deep metric learning networks that accounts for the nuances of metric losses.

Metric Learning

Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems

no code implementations16 Dec 2020 Yuzhe ma, Jon Sharp, Ruizhe Wang, Earlence Fernandes, Xiaojin Zhu

In this paper, we study adversarial attacks on KF as part of the more complex machine-human hybrid system of Forward Collision Warning.

Autonomous Vehicles Model Predictive Control

Data Privacy in Trigger-Action Systems

1 code implementation10 Dec 2020 Yunang Chen, Amrita Roy Chowdhury, Ruizhe Wang, Andrei Sabelfeld, Rahul Chatterjee, Earlence Fernandes

Trigger-action platforms (TAPs) allow users to connect independent web-based or IoT services to achieve useful automation.

Cryptography and Security

Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect

2 code implementations CVPR 2021 Athena Sayles, Ashish Hooda, Mohit Gupta, Rahul Chatterjee, Earlence Fernandes

By contrast, we contribute a procedure to generate, for the first time, physical adversarial examples that are invisible to human eyes.


GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems

1 code implementation17 Feb 2020 Ryan Feng, Neal Mangaokar, Jiefeng Chen, Earlence Fernandes, Somesh Jha, Atul Prakash

We address three key requirements for practical attacks for the real-world: 1) automatically constraining the size and shape of the attack so it can be applied with stickers, 2) transform-robustness, i. e., robustness of a attack to environmental physical variations such as viewpoint and lighting changes, and 3) supporting attacks in not only white-box, but also black-box hard-label scenarios, so that the adversary can attack proprietary models.

BIG-bench Machine Learning General Classification +1

Analyzing the Interpretability Robustness of Self-Explaining Models

no code implementations27 May 2019 Haizhong Zheng, Earlence Fernandes, Atul Prakash

Recently, interpretable models called self-explaining models (SEMs) have been proposed with the goal of providing interpretability robustness.

Program Analysis of Commodity IoT Applications for Security and Privacy: Challenges and Opportunities

1 code implementation18 Sep 2018 Z. Berkay Celik, Earlence Fernandes, Eric Pauley, Gang Tan, Patrick McDaniel

Based on a study of five IoT programming platforms, we identify the key insights resulting from works in both the program analysis and security communities and relate the efficacy of program-analysis techniques to security and privacy issues.

Cryptography and Security Programming Languages

Physical Adversarial Examples for Object Detectors

no code implementations20 Jul 2018 Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Florian Tramer, Atul Prakash, Tadayoshi Kohno, Dawn Song

In this work, we extend physical attacks to more challenging object detection models, a broader class of deep learning algorithms widely used to detect and label multiple objects within a scene.

Object object-detection +1

Robust Physical-World Attacks on Deep Learning Visual Classification

no code implementations CVPR 2018 Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, Dawn Song

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.

Classification General Classification +1

Tyche: Risk-Based Permissions for Smart Home Platforms

no code implementations14 Jan 2018 Amir Rahmati, Earlence Fernandes, Kevin Eykholt, Atul Prakash

When using risk-based permissions, device operations are grouped into units of similar risk, and users grant apps access to devices at that risk-based granularity.

Cryptography and Security

Note on Attacking Object Detectors with Adversarial Stickers

no code implementations21 Dec 2017 Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Dawn Song, Tadayoshi Kohno, Amir Rahmati, Atul Prakash, Florian Tramer

Given the fact that state-of-the-art objection detection algorithms are harder to be fooled by the same set of adversarial examples, here we show that these detectors can also be attacked by physical adversarial examples.

Object Test

Robust Physical-World Attacks on Deep Learning Models

1 code implementation27 Jul 2017 Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, Dawn Song

We propose a general attack algorithm, Robust Physical Perturbations (RP2), to generate robust visual adversarial perturbations under different physical conditions.


Internet of Things Security Research: A Rehash of Old Ideas or New Intellectual Challenges?

no code implementations23 May 2017 Earlence Fernandes, Amir Rahmati, Kevin Eykholt, Atul Prakash

The Internet of Things (IoT) is a new computing paradigm that spans wearable devices, homes, hospitals, cities, transportation, and critical infrastructure.

Cryptography and Security

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