b'Somesh Jha'

research

∙ 05/18/2022

Constraining the Attack Space of Machine Learning Models with Distribution Clamping Preprocessing

Preprocessing and outlier detection techniques have both been applied to...

Ryan Feng, et al. ∙

research

∙ 03/04/2022

Concept-based Explanations for Out-Of-Distribution Detectors

Out-of-distribution (OOD) detection plays a crucial role in ensuring the...

Jihye Choi, et al. ∙

research

∙ 03/02/2022

A Quantitative Geometric Approach to Neural Network Smoothness

Fast and precise Lipschitz constant estimation of neural networks is an ...

Zi Wang, et al. ∙

research

∙ 02/21/2022

Using Illustrations to Communicate Differential Privacy Trust Models: An Investigation of Users' Comprehension, Perception, and Data Sharing Decision

Proper communication is key to the adoption and implementation of differ...

Aiping Xiong, et al. ∙

research

∙ 02/11/2022

Towards Adversarially Robust Deepfake Detection: An Ensemble Approach

Detecting deepfakes is an important problem, but recent work has shown t...

Ashish Hooda, et al. ∙

research

∙ 02/09/2022

An Exploration of Multicalibration Uniform Convergence Bounds

Recent works have investigated the sample complexity necessary for fair ...

Harrison Rosenberg, et al. ∙

research

∙ 12/23/2021

EIFFeL: Ensuring Integrity for Federated Learning

Federated learning (FL) enables clients to collaborate with a server to ...

Amrita Roy Chowdhury, et al. ∙

research

∙ 09/06/2021

Lightweight, Multi-Stage, Compiler-Assisted Application Specialization

Program debloating aims to enhance the performance and reduce the attack...

Mohannad Alhanahnah, et al. ∙

research

∙ 08/16/2021

NeuraCrypt is not private

NeuraCrypt (Yara et al. arXiv 2021) is an algorithm that converts a sens...

Nicholas Carlini, et al. ∙

research

∙ 08/05/2021

Fairness Properties of Face Recognition and Obfuscation Systems

The proliferation of automated facial recognition in various commercial ...

Harrison Rosenberg, et al. ∙

research

∙ 08/02/2021

Domain Adaptation for Autoencoder-Based End-to-End Communication Over Wireless Channels

The problem of domain adaptation conventionally considers the setting wh...

Jayaram Raghuram, et al. ∙

research

∙ 06/11/2021

A Shuffling Framework for Local Differential Privacy

ldp deployments are vulnerable to inference attacks as an adversary can ...

Casey Meehan, et al. ∙

research

∙ 03/04/2021

Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples

Designing deep networks robust to adversarial examples remains an open p...

Washington Garcia, et al. ∙

research

∙ 02/14/2021

Exploring Adversarial Robustness of Deep Metric Learning

Deep Metric Learning (DML), a widely-used technique, involves learning a...

Thomas Kobber Panum, et al. ∙

research

∙ 02/09/2021

CaPC Learning: Confidential and Private Collaborative Learning

Machine learning benefits from large training datasets, which may not al...

Christopher A. Choquette-Choo, et al. ∙

research

∙ 12/19/2020

Sample Complexity of Adversarially Robust Linear Classification on Separated Data

We consider the sample complexity of learning with adversarial robustnes...

Robi Bhattacharjee, et al. ∙

research

∙ 11/11/2020

ShadowNet: A Secure and Efficient System for On-device Model Inference

On-device machine learning (ML) is getting more and more popular as fast...

Zhichuang Sun, et al. ∙

research

∙ 09/11/2020

Intertwining Order Preserving Encryption and Differential Privacy

Ciphertexts of an order-preserving encryption (OPE) scheme preserve the ...

Amrita Roy Chowdhury, et al. ∙

research

∙ 07/29/2020

Detecting Anomalous Inputs to DNN Classifiers By Joint Statistical Testing at the Layers

Detecting anomalous inputs, such as adversarial and out-of-distribution ...

Jayaram Raghuram, et al. ∙

research

∙ 07/12/2020

Abstract Universal Approximation for Neural Networks

With growing concerns about the safety and robustness of neural networks...

Zi Wang, et al. ∙

research

∙ 07/01/2020

Robust Learning against Logical Adversaries

Test-time adversarial attacks have posed serious challenges to the robus...

Yizhen Wang, et al. ∙

research

∙ 05/24/2020

Continuous Release of Data Streams under both Centralized and Local Differential Privacy

In this paper, we study the problem of publishing a stream of real-value...

Tianhao Wang, et al. ∙

research

∙ 03/31/2020

Towards Effective Differential Privacy Communication for Users' Data Sharing Decision and Comprehension

Differential privacy protects an individual's privacy by perturbing data...

Aiping Xiong, et al. ∙

research

∙ 03/26/2020

Obliviousness Makes Poisoning Adversaries Weaker

Poisoning attacks have emerged as a significant security threat to machi...

Sanjam Garg, et al. ∙

research

∙ 03/19/2020

Face-Off: Adversarial Face Obfuscation

Advances in deep learning have made face recognition increasingly feasib...

Chuhan Gao, et al. ∙

research

∙ 03/03/2020

Analyzing Accuracy Loss in Randomized Smoothing Defenses

Recent advances in machine learning (ML) algorithms, especially deep neu...

Yue Gao, et al. ∙

research

∙ 02/18/2020

CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods

We study the problem of learning Granger causality between event types f...

Wei Zhang, et al. ∙

research

∙ 02/17/2020

Query-Efficient Physical Hard-Label Attacks on Deep Learning Visual Classification

We present Survival-OPT, a physical adversarial example algorithm in the...

Ryan Feng, et al. ∙

research

∙ 02/07/2020

Semantic Robustness of Models of Source Code

Deep neural networks are vulnerable to adversarial examples - small inpu...

Goutham Ramakrishnan, et al. ∙

research

∙ 10/02/2019

Generating Semantic Adversarial Examples with Differentiable Rendering

Machine learning (ML) algorithms, especially deep neural networks, have ...

Lakshya Jain, et al. ∙

research

∙ 09/07/2019

On Need for Topology Awareness of Generative Models

Manifold assumption in learning states that: the data lie approximately ...

Uyeong Jang, et al. ∙

research

∙ 09/07/2019

On Need for Topology-Aware Generative Models for Manifold-Based Defenses

ML algorithms or models, especially deep neural networks (DNNs), have sh...

Uyeong Jang, et al. ∙

research

∙ 08/30/2019

MURS: Practical and Robust Privacy Amplification with Multi-Party Differential Privacy

When collecting information, local differential privacy (LDP) alleviates...

Tianhao Wang, et al. ∙

research

∙ 08/30/2019

Practical and Robust Privacy Amplification with Multi-Party Differential Privacy

When collecting information, local differential privacy (LDP) alleviates...

Tianhao Wang, et al. ∙

research

∙ 05/30/2019

Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models

Directed graphical models (DGMs) are a class of probabilistic models tha...

Amrita Roy Chowdhury, et al. ∙

research

∙ 05/28/2019

Adversarially Robust Learning Could Leverage Computational Hardness

Over recent years, devising classification algorithms that are robust to...

Sanjam Garg, et al. ∙

research

∙ 05/26/2019

Enhancing ML Robustness Using Physical-World Constraints

Recent advances in Machine Learning (ML) have demonstrated that neural n...

Varun Chandrasekaran, et al. ∙

research

∙ 05/23/2019

Robust Attribution Regularization

An emerging problem in trustworthy machine learning is to train models t...

Jiefeng Chen, et al. ∙

research

∙ 03/14/2019

Attribution-driven Causal Analysis for Detection of Adversarial Examples

Attribution methods have been developed to explain the decision of a mac...

Susmit Jha, et al. ∙

research

∙ 02/20/2019

Outis: Crypto-Assisted Differential Privacy on Untrusted Servers

Differential privacy has steadily become the de-facto standard for achie...

Amrita Roy Chowdhury, et al. ∙

research

∙ 11/21/2018

Privacy-Preserving Collaborative Prediction using Random Forests

We study the problem of privacy-preserving machine learning (PPML) for e...

Irene Giacomelli, et al. ∙

research

∙ 11/05/2018

Model Extraction and Active Learning

Machine learning is being increasingly used by individuals, research ins...

Varun Chandrasekaran, et al. ∙

research

∙ 10/15/2018

Adversarial Learning and Explainability in Structured Datasets

We theoretically and empirically explore the explainability benefits of ...

Prasad Chalasani, et al. ∙

research

∙ 09/28/2018

Explainable Black-Box Attacks Against Model-based Authentication

Establishing unique identities for both humans and end systems has been ...

Washington Garcia, et al. ∙

research

∙ 09/21/2018

Adversarial Binaries for Authorship Identification

Binary code authorship identification determines authors of a binary pro...

Xiaozhu Meng, et al. ∙

research

∙ 09/11/2018

Neural-Augmented Static Analysis of Android Communication

We address the problem of discovering communication links between applic...

Jinman Zhao, et al. ∙

research

∙ 05/20/2018

Improving Adversarial Robustness by Data-Specific Discretization

A recent line of research proposed (either implicitly or explicitly) gra...

Jiefeng Chen, et al. ∙

research

∙ 04/19/2018

Semantic Adversarial Deep Learning

Fueled by massive amounts of data, models produced by machine-learning (...

Tommaso Dreossi, et al. ∙

research

∙ 02/09/2018

OEI: Operation Execution Integrity for Embedded Devices

We formulate a new security property, called "Operation Execution Integr...

Zhichuang Sun, et al. ∙

research

∙ 11/21/2017

The Manifold Assumption and Defenses Against Adversarial Perturbations

In the adversarial-perturbation problem of neural networks, an adversary...

Xi Wu, et al. ∙

Somesh Jha

Featured Co-authors