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Found 10 papers, 8 papers with code
PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses
State-of-the-art defenses against adversarial patch attacks can now achieve strong certifiable robustness with a marginal drop in model utility.
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks
Using our framework, we present the first leaderboard, MultiRobustBench, for benchmarking multiattack evaluation which captures performance across attack types and attack strengths.
ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking
An attacker can use a single physically-realizable adversarial patch to make the object detector miss the detection of victim objects and undermine the functionality of object detection applications.
PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier
Remarkably, PatchCleanser achieves 83. 9% top-1 clean accuracy and 62. 1% top-1 certified robust accuracy against a 2%-pixel square patch anywhere on the image for the 1000-class ImageNet dataset.
PatchGuard++: Efficient Provable Attack Detection against Adversarial Patches
Recent provably robust defenses generally follow the PatchGuard framework by using CNNs with small receptive fields and secure feature aggregation for robust model predictions.
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
We circumvent this challenge by using additional data from proxy distributions learned by advanced generative models.
DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks
In this paper, we propose DetectorGuard as the first general framework for building provably robust object detectors against localized patch hiding attacks.
PatchGuard: A Provably Robust Defense against Adversarial Patches via Small Receptive Fields and Masking
In this paper, we propose a general defense framework called PatchGuard that can achieve high provable robustness while maintaining high clean accuracy against localized adversarial patches.
Differentially Private Data Generative Models
We conjecture that the key to defend against the model inversion and GAN-based attacks is not due to differential privacy but the perturbation of training data.
Generating 3D Adversarial Point Clouds
Deep neural networks are known to be vulnerable to adversarial examples which are carefully crafted instances to cause the models to make wrong predictions.
