activity
20162024
most citedUPSET and ANGRI : Breaking High Performance Image Classifiers

91 citations · 296 across the 23 of their papers we have counts for

collaborators
Showing 2021Show all

6 papers · 1 filter

cs.LG20211 cited

Identification of Attack-Specific Signatures in Adversarial Examples

Hossein Souri, Pirazh Khorramshahi, Chun Pong Lau +2

The adversarial attack literature contains a myriad of algorithms for crafting perturbations which yield pathological behavior in neural networks. In many cases, multiple algorithm…

cs.CV2021

LR-to-HR Face Hallucination with an Adversarial Progressive Attribute-Induced Network

Nitin Balachandran, Jun-Cheng Chen, Rama Chellappa

Face super-resolution is a challenging and highly ill-posed problem since a low-resolution (LR) face image may correspond to multiple high-resolution (HR) ones during the hallucina…

cs.CV2021

Finding Facial Forgery Artifacts with Parts-Based Detectors

Steven Schwarcz, Rama Chellappa

Manipulated videos, especially those where the identity of an individual has been modified using deep neural networks, are becoming an increasingly relevant threat in the modern da…

cs.CV2021

PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition

Prithviraj Dhar, Joshua Gleason, Aniket Roy +2

Face recognition networks encode information about sensitive attributes while being trained for identity classification. Such encoding has two major issues: (a) it makes the face r…

cs.LG20211 cited

To Boost or not to Boost: On the Limits of Boosted Neural Networks

Sai Saketh Rambhatla, Michael Jones, Rama Chellappa

Boosting is a method for finding a highly accurate hypothesis by linearly combining many ``weak" hypotheses, each of which may be only moderately accurate. Thus, boosting is a meth…

cs.CV20215 cited

Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer

Arthita Ghosh, Max Ehrlich, Larry Davis +1

Urban material recognition in remote sensing imagery is a highly relevant, yet extremely challenging problem due to the difficulty of obtaining human annotations, especially on low…