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 751–800 of 1808 papers

TitleStatusHype
Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning—0
Adversarially Robust Classification by Conditional Generative Model Inversion—0
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples—0
Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples—0
Democratic Training Against Universal Adversarial Perturbations—0
Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network—0
Exploiting Vulnerability of Pooling in Convolutional Neural Networks by Strict Layer-Output Manipulation for Adversarial Attacks—0
ExploreADV: Towards exploratory attack for Neural Networks—0
GradMDM: Adversarial Attack on Dynamic Networks—0
Exploring Adversarial Attacks against Latent Diffusion Model from the Perspective of Adversarial Transferability—0
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers—0
Exploring Adversarial Fake Images on Face Manifold—0
Exploring Adversarial Threat Models in Cyber Physical Battery Systems—0
Analyzing the Noise Robustness of Deep Neural Networks—0
Exploring Frequency Adversarial Attacks for Face Forgery Detection—0
Exploring High-Order Structure for Robust Graph Structure Learning—0
Delving into Data: Effectively Substitute Training for Black-box Attack—0
A Context-Aware Approach for Textual Adversarial Attack through Probability Difference Guided Beam Search—0
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models—0
Attention, Please! Adversarial Defense via Activation Rectification and Preservation—0
Extreme Miscalibration and the Illusion of Adversarial Robustness—0
FABLE: A Localized, Targeted Adversarial Attack on Weather Forecasting Models—0
FaceGuard: A Self-Supervised Defense Against Adversarial Face Images—0
FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning—0
Fair Robust Active Learning by Joint Inconsistency—0
Faithfulness and the Notion of Adversarial Sensitivity in NLP Explanations—0
Fall Leaf Adversarial Attack on Traffic Sign Classification—0
Audio Adversarial Examples: Attacks Using Vocal Masks—0
Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models—0
Defensive Quantization: When Efficiency Meets Robustness—0
Adversarial Attack with Raindrops—0
Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense—0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—0
Headless Horseman: Adversarial Attacks on Transfer Learning Models—0
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection—0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense—0
Defense-guided Transferable Adversarial Attacks—0
Feature Importance Guided Attack: A Model Agnostic Adversarial Attack—0
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks—0
Adversarial Attack with Pattern Replacement—0
Feature Unlearning for Pre-trained GANs and VAEs—0
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods—0
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems—0
AutoAugment Input Transformation for Highly Transferable Targeted Attacks—0
Global Robustness Verification Networks—0
Few-Features Attack to Fool Machine Learning Models through Mask-Based GAN—0
Defense Against Explanation Manipulation—0
F&F Attack: Adversarial Attack against Multiple Object Trackers by Inducing False Negatives and False Positives—0
Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection—0
Analysis of the vulnerability of machine learning regression models to adversarial attacks using data from 5G wireless networks—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