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

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Showing 851–900 of 1808 papers

TitleStatusHype
GradMDM: Adversarial Attack on Dynamic Networks—0
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off—0
Adversarial Robustness for Machine Learning Cyber Defenses Using Log Data—0
A Differentiable Language Model Adversarial Attack on Text Classifiers—0
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks—0
Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense—0
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning—0
Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world—0
Golden Ratio Search: A Low-Power Adversarial Attack for Deep Learning based Modulation Classification—0
Global Robustness Verification Networks—0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems—0
Adversarial Robustness for Deep Learning-based Wildfire Prediction Models—0
Generative Adversarial Patches for Physical Attacks on Cross-Modal Pedestrian Re-Identification—0
Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing—0
Generating Watermarked Adversarial Texts—0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—0
Generating Valid and Natural Adversarial Examples with Large Language Models—0
Benign Adversarial Attack: Tricking Models for Goodness—0
Generating Unrestricted Adversarial Examples via Three Parameters—0
Generating Semantically Valid Adversarial Questions for TableQA—0
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection—0
Adversarial Relighting Against Face Recognition—0
AdversariaL attacK sAfety aLIgnment(ALKALI): Safeguarding LLMs through GRACE: Geometric Representation-Aware Contrastive Enhancement- Introducing Adversarial Vulnerability Quality Index (AVQI)—0
Generating Semantic Adversarial Examples via Feature Manipulation—0
Generating Out of Distribution Adversarial Attack using Latent Space Poisoning—0
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks—0
Benchmarking Adversarial Robustness—0
Adversarial RAW: Image-Scaling Attack Against Imaging Pipeline—0
Generating Black-Box Adversarial Examples in Sparse Domain—0
Harmonic Adversarial Attack Method—0
Generating Adversarial Inputs Using A Black-box Differential Technique—0
Generating Adversarial Examples with an Optimized Quality—0
Generating Adversarial Attacks in the Latent Space—0
Benchmarking Adversarially Robust Quantum Machine Learning at Scale—0
Adversarial Attack on Skeleton-based Human Action Recognition—0
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds—0
Generalization to Mitigate Synonym Substitution Attacks—0
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework—0
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks—0
BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization—0
General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments—0
Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment—0
Hiding Backdoors within Event Sequence Data via Poisoning Attacks—0
CE-based white-box adversarial attacks will not work using super-fitting—0
Adversarial Profiles: Detecting Out-Distribution & Adversarial Samples in Pre-trained CNNs—0
Adversarial Attack on Sentiment Classification—0
A Deep Genetic Programming based Methodology for Art Media Classification Robust to Adversarial Perturbations—0
A Black-Box Attack on Optical Character Recognition Systems—0
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection—0
GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximization—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