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 1026–1050 of 1808 papers

TitleStatusHype
Learning to Defend by Learning to Attack—0
Learning to Defense by Learning to Attack—0
Learning to Detect Adversarial Examples Based on Class Scores—0
Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization—0
Visual Adversarial Attack on Vision-Language Models for Autonomous Driving—0
Visual Attack and Defense on Text—0
Thundernna: a white box adversarial attack—0
Thwarting finite difference adversarial attacks with output randomization—0
Time-aware Gradient Attack on Dynamic Network Link Prediction—0
Left-right Discrepancy for Adversarial Attack on Stereo Networks—0
Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies—0
Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend—0
To be Robust and to be Fair: Aligning Fairness with Robustness—0
LFAA: Crafting Transferable Targeted Adversarial Examples with Low-Frequency Perturbations—0
Patch Synthesis for Property Repair of Deep Neural Networks—0
To Each (Textual Sequence) Its Own: Improving Memorized-Data Unlearning in Large Language Models—0
Light Lies: Optical Adversarial Attack—0
BOSH: An Efficient Meta Algorithm for Decision-based Attacks—0
OTAD: An Optimal Transport-Induced Robust Model for Agnostic Adversarial Attack—0
Limited Budget Adversarial Attack Against Online Image Stream—0
Linear Backpropagation Leads to Faster Convergence—0
Linear system security -- detection and correction of adversarial attacks in the noise-free case—0
LLMs Can Defend Themselves Against Jailbreaking in a Practical Manner: A Vision Paper—0
Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing—0
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning—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