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 1351–1375 of 1808 papers

TitleStatusHype
Learn2Weight: Weights Transfer Defense against Similar-domain Adversarial Attacks—0
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations—0
Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing—0
Learning Globally Optimized Language Structure via Adversarial Training—0
Learning Key Steps to Attack Deep Reinforcement Learning Agents—0
Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations—0
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
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
LFAA: Crafting Transferable Targeted Adversarial Examples with Low-Frequency Perturbations—0
Light Lies: Optical 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
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning—0
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning—0
Localized Adversarial Training for Increased Accuracy and Robustness in Image Classification—0
LocalStyleFool: Regional Video Style Transfer Attack Using Segment Anything Model—0
Looking From the Future: Multi-order Iterations Can Enhance Adversarial Attack Transferability—0
L_p-norm Distortion-Efficient Adversarial Attack—0
L-RED: Efficient Post-Training Detection of Imperceptible Backdoor Attacks without Access to the Training Set—0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet20Test Accuracy89.9589.95(1)Community Verified
2Xu et al.Attack: PGD2078.68—Unverified
33-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
4TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
5AdvTraining [madry2018]Attack: PGD2048.44—Unverified
6TRADES [zhang2019b]Attack: PGD2045.9—Unverified
7XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified