Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks
Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.
REAP: A Large-Scale Realistic Adversarial Patch Benchmark
A novel physical adversarial attack tackling the Digital-to-Physical Visual Inconsistency problem.
A pipeline for generating inconspicuous naturalistic adversarial patches (INAPs) against object detectors with one input image
experimenting effects of adversarial patch attacks to some of targets on python (pytorch)
Adversarial Patch defense using SegmentAndComplete (SAC) & Masked AutoEncoder (MAE)
Demonstrates the training and exploitation of a poisoned machine learning model.
Add a description, image, and links to the adversarial-patches topic page so that developers can more easily learn about it.
To associate your repository with the adversarial-patches topic, visit your repo's landing page and select "manage topics."