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
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey—0
Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification—0
Enhancing Tracking Robustness with Auxiliary Adversarial Defense Networks—0
Conformal Shield: A Novel Adversarial Attack Detection Framework for Automatic Modulation Classification—0
Extreme Miscalibration and the Illusion of Adversarial Robustness—0
Improving the JPEG-resistance of Adversarial Attacks on Face Recognition by Interpolation Smoothing—0
RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage GenerationCode1
LLMs Can Defend Themselves Against Jailbreaking in a Practical Manner: A Vision Paper—0
Fast Adversarial Attacks on Language Models In One GPU MinuteCode2
Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Task—0
Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated PoliciesCode0
An Adversarial Approach to Evaluating the Robustness of Event Identification Models—0
AICAttack: Adversarial Image Captioning Attack with Attention-Based OptimizationCode0
Only My Model On My Data: A Privacy Preserving Approach Protecting one Model and Deceiving Unauthorized Black-Box Models—0
Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensembleCode0
Corruption Robust Offline Reinforcement Learning with Human Feedback—0
TETRIS: Towards Exploring the Robustness of Interactive Segmentation—0
FoolSDEdit: Deceptively Steering Your Edits Towards Targeted Attribute-aware Distribution—0
PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks—0
DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms in Vision TransformersCode0
Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models—0
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on TextCode0
On the Multi-modal Vulnerability of Diffusion ModelsCode1
SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign DecodingCode0
STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition—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