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computer-vision-1

Adversarial Attack Detection

3260 papers • 126 benchmarks • 313 datasets

The detection of adversarial attacks.

(Image credit: Papersgraph)

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Most implemented papers

Maximum Mean Discrepancy Test is Aware of Adversarial Attacks

Tongliang Liu, Masashi Sugiyama, Bo Han, Gang Niu, Ruize Gao, Feng Liu, Jingfeng Zhang•Wed Oct 21 2020

It is verified that the MMD test is aware of adversarial attacks, which lights up a novel road for adversarial data detection based on two-sample tests.

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Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?

Janis Keuper, P. Lorenz, Dominik Strassel, Margret Keuper•Wed Dec 01 2021

It is argued that the alternation of data by AutoAttack with l-inf, eps = 8/255 is unrealistically strong, resulting in close to perfect detection rates of adversarial samples even by simple detection algorithms and human observers, and that other attack methods are much harder to detect while achieving similar success rates.

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Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness

A. Malinin, M. Gales•Thu May 30 2019

This paper investigates using Prior Networks to detect adversarial attacks and proposes a generalized form of adversarial training, and shows that the appropriate training criterion for Prior Networks is the reverse KL-divergence between Dirichlet distributions.

206 0

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MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks

Hailin Shi, Chenxu Zhao, Chen Ma, Li Chen, Junhai Yong, Dan Zeng•Mon Aug 05 2019

This work proposes a meta-learning based robust detection method to detect new adversarial attacks with limited examples that consists of a double-network framework: a task-dedicated network and a master network which alternatively learn the detection capability for either seen attack or a new attack.

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Towards Feature Space Adversarial Attack

X. Zhang, Siyuan Cheng, Qiuling Xu, Guanhong Tao, Lin Tan•Sat Apr 25 2020

This work proposes a new type of adversarial attack to Deep Neural Networks for image classification that induces model misclassfication by injecting style changes insensitive for humans, through an optimization procedure.

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Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view Inconsistency

Sohaib Kiani, S. Awan, Chao Lan, Fengjun Li, Bo Luo•Fri Sep 24 2021

Argos first amplifies the discrepancies between the visual content of an image and its misclassified label induced by the attack using a set of regeneration mechanisms and then identifies an image as adversarial if the reproduced views deviate to a preset degree.

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Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection

S. Feizi, Jiangjiang Liu, Ramalingam Chellappa, Alexander Levine, Chun Pong Lau•Tue Dec 07 2021

This paper proposes Segment and Complete defense (SAC), a general framework for defending object detectors against patch attacks through detection and removal of adversarial patches, and presents the APRICOT-Mask dataset, which augments the APRicOT dataset with pixel-level annotations of adversaria patches.

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Residue-Based Natural Language Adversarial Attack Detection

M. Gales, Vyas Raina•Sat Apr 16 2022

This work proposes a simple sentence-embedding “residue” based detector to identify adversarial examples that out-performs ported image domain detectors and recent state of the art NLP specific detectors.

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Adversarial Attack Detection | State-of-the-Art