197 citations · 299 across the 47 of their papers we have counts for
12 papers · 1 filter
Prediction with Expert Advice under Local Differential Privacy
Ben Jacobsen, Kassem Fawaz
We study the classic problem of prediction with expert advice under the constraint of local differential privacy (LDP). In this context, we first show that a classical algorithm na…
What Really is a Member? Discrediting Membership Inference via Poisoning
Neal Mangaokar, Ashish Hooda, Zhuohang Li +5
Membership inference tests aim to determine whether a particular data point was included in a language model's training set. However, recent works have shown that such tests often…
A Picture is Worth 500 Labels: A Case Study of Demographic Disparities in Local Machine Learning Models for Instagram and TikTok
Jack West, Lea Thiemt, Shimaa Ahmed +3
Mobile apps have embraced user privacy by moving their data processing to the user's smartphone. Advanced machine learning (ML) models, such as vision models, can now locally analy…
SEA: Shareable and Explainable Attribution for Query-based Black-box Attacks
Yue Gao, Ilia Shumailov, Kassem Fawaz
Machine Learning (ML) systems are vulnerable to adversarial examples, particularly those from query-based black-box attacks. Despite various efforts to detect and prevent such atta…
Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks
Ashish Hooda, Neal Mangaokar, Ryan Feng +3
Adversarial examples threaten the integrity of machine learning systems with alarming success rates even under constrained black-box conditions. Stateful defenses have emerged as a…
On the Limitations of Stochastic Pre-processing Defenses
Yue Gao, Ilia Shumailov, Kassem Fawaz +1
Defending against adversarial examples remains an open problem. A common belief is that randomness at inference increases the cost of finding adversarial inputs. An example of such…
An Exploration of Multicalibration Uniform Convergence Bounds
Harrison Rosenberg, Robi Bhattacharjee, Kassem Fawaz +1
Recent works have investigated the sample complexity necessary for fair machine learning. The most advanced of such sample complexity bounds are developed by analyzing multicalibra…
D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles
Ashish Hooda, Neal Mangaokar, Ryan Feng +3
Detecting diffusion-generated deepfake images remains an open problem. Current detection methods fail against an adversary who adds imperceptible adversarial perturbations to the d…
Tubes Among Us: Analog Attack on Automatic Speaker Identification
Shimaa Ahmed, Yash Wani, Ali Shahin Shamsabadi +4
Recent years have seen a surge in the popularity of acoustics-enabled personal devices powered by machine learning. Yet, machine learning has proven to be vulnerable to adversarial…
Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems
Yue Gao, Ilia Shumailov, Kassem Fawaz
As real-world images come in varying sizes, the machine learning model is part of a larger system that includes an upstream image scaling algorithm. In this paper, we investigate t…
Analyzing Accuracy Loss in Randomized Smoothing Defenses
Yue Gao, Harrison Rosenberg, Kassem Fawaz +2
Recent advances in machine learning (ML) algorithms, especially deep neural networks (DNNs), have demonstrated remarkable success (sometimes exceeding human-level performance) on s…
Rearchitecting Classification Frameworks For Increased Robustness
Varun Chandrasekaran, Brian Tang, Nicolas Papernot +3
While generalizing well over natural inputs, neural networks are vulnerable to adversarial inputs. Existing defenses against adversarial inputs have largely been detached from the…