Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.
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Updated
Oct 3, 2023 - Python
Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks
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)
Demonstrates the training and exploitation of a poisoned machine learning model.
Adversarial Patch defense using SegmentAndComplete (SAC) & Masked AutoEncoder (MAE)
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