Understanding the Impact of Compression on Feature Detection and Matching in Computer Vision. - Wu-chi Feng; Ryan Feng; Paul Wyatt; Feng Liu 015 (ISM, 2016)

ISIFT: extracting incremental results from SIFT. - Ben Hamlin; Ryan Feng; Wu-chi Feng (MMSys, 2018)

Leveraging Image Processing Techniques to Thwart Adversarial Attacks in Image Classification. - Yeganeh Jalalpour; Li-Yun Wang; Ryan Feng; Wu-chi Feng (ISM, 2019)

Robot-Assisted Feeding: Generalizing Skewering Strategies Across Food Items on a Plate. - Ryan Feng; Youngsun Kim; Gilwoo Lee; Ethan K. Gordon; Matt Schmittle; Shivaum Kumar; Tapomayukh Bhattacharjee; Siddhartha S. Srinivasa (ISRR, 2019)

Robot-Assisted Feeding: Generalizing Skewering Strategies across Food Items on a Realistic Plate. - Ryan Feng; Youngsun Kim; Gilwoo Lee; Ethan K. Gordon; Matt Schmittle; Shivaum Kumar; Tapomayukh Bhattacharjee; Siddhartha S. Srinivasa (CoRR, 2019)

Query-Efficient Physical Hard-Label Attacks on Deep Learning Visual Classification. - Ryan Feng; Jiefeng Chen 001; Nelson R. Manohar; Earlence Fernandes; Somesh Jha; Atul Prakash 001 (CoRR, 2020)

Essential Features: Reducing the Attack Surface of Adversarial Perturbations with Robust Content-Aware Image Preprocessing. - Ryan Feng; Wu-chi Feng; Atul Prakash 001 (CoRR, 2020)

Smart Black Box 2.0: Efficient High-Bandwidth Driving Data Collection Based on Video Anomalies. - Ryan Feng; Yu Yao 006; Ella M. Atkins (Algorithms, 2021)

Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples. - Nelson Manohar-Alers; Ryan Feng; Sahib Singh; Jiguo Song; Atul Prakash 001 (CoRR, 2021)

GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems. - Ryan Feng; Neal Mangaokar; Jiefeng Chen 001; Earlence Fernandes; Somesh Jha; Atul Prakash 001 (EuroS&P, 2022)

Towards Adversarially Robust Deepfake Detection: An Ensemble Approach. - Ashish Hooda; Neal Mangaokar; Ryan Feng; Kassem Fawaz; Somesh Jha; Atul Prakash 001 (CoRR, 2022)

Constraining the Attack Space of Machine Learning Models with Distribution Clamping Preprocessing. - Ryan Feng; Somesh Jha; Atul Prakash 001 (CoRR, 2022)

Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks. - Ryan Feng; Ashish Hooda; Neal Mangaokar; Kassem Fawaz; Somesh Jha; Atul Prakash 001 (CCS, 2023)

Concept-based Explanations for Out-of-Distribution Detectors. - Jihye Choi; Jayaram Raghuram; Ryan Feng; Jiefeng Chen 001; Somesh Jha; Atul Prakash 001 (ICML, 2023)

Investigating Stateful Defenses Against Black-Box Adversarial Examples. - Ryan Feng; Ashish Hooda; Neal Mangaokar; Kassem Fawaz; Somesh Jha; Atul Prakash 001 (CoRR, 2023)

Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks. - Ashish Hooda; Neal Mangaokar; Ryan Feng; Kassem Fawaz; Somesh Jha; Atul Prakash 001 (CoRR, 2023)

Achieving the Safety and Security of the End-to-End AV Pipeline. - Noah T. Curran; Minkyoung Cho; Ryan Feng; Liangkai Liu; Brian Jay Tang; Pedram MohajerAnsari; Alkim Domeke; Mert D. Pesé; Kang G. Shin (CSCS, 2024)

D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles. - Ashish Hooda; Neal Mangaokar; Ryan Feng; Kassem Fawaz; Somesh Jha; Atul Prakash 001 (WACV, 2024)

Test-Time Canonicalization by Foundation Models for Robust Perception. - Utkarsh Singhal; Ryan Feng; Stella X. Yu; Atul Prakash 001 (CoRR, 2025)