SOTAVerified

Adversarial Attack

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 376–400 of 1808 papers

TitleStatusHype
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons—0
Adversarial Attacks Against Deep Learning Systems for ICD-9 Code Assignment—0
Adversarial Robustness for Machine Learning Cyber Defenses Using Log Data—0
A Differentiable Language Model Adversarial Attack on Text Classifiers—0
Mitigating Deep Learning Vulnerabilities from Adversarial Examples Attack in the Cybersecurity Domain—0
Btech thesis report on adversarial attack detection and purification of adverserially attacked images—0
Adversarial Robustness for Deep Learning-based Wildfire Prediction Models—0
AdversariaL attacK sAfety aLIgnment(ALKALI): Safeguarding LLMs through GRACE: Geometric Representation-Aware Contrastive Enhancement- Introducing Adversarial Vulnerability Quality Index (AVQI)—0
Adversarial Relighting Against Face Recognition—0
A Deep Genetic Programming based Methodology for Art Media Classification Robust to Adversarial Perturbations—0
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework—0
Adversarial RAW: Image-Scaling Attack Against Imaging Pipeline—0
Adversarial Attack on Skeleton-based Human Action Recognition—0
Adversarial Profiles: Detecting Out-Distribution & Adversarial Samples in Pre-trained CNNs—0
Adversarial Attack on Sentiment Classification—0
A Black-Box Attack on Optical Character Recognition Systems—0
Brightness-Restricted Adversarial Attack Patch—0
BufferSearch: Generating Black-Box Adversarial Texts With Lower Queries—0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
Adversarial Patch Attacks on Monocular Depth Estimation Networks—0
Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack—0
Adversarial optimization leads to over-optimistic security-constrained dispatch, but sampling can help—0
Adversarial Neon Beam: A Light-based Physical Attack to DNNs—0
Adaptive Perturbation for Adversarial Attack—0
Adversarial Music: Real World Audio Adversary Against Wake-word Detection System—0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68—Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
4AdvTraining [madry2018]Attack: PGD2048.44—Unverified
5TRADES [zhang2019b]Attack: PGD2045.9—Unverified
6XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified