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 476–500 of 1808 papers

TitleStatusHype
Fall Leaf Adversarial Attack on Traffic Sign Classification—0
Visual Adversarial Attack on Vision-Language Models for Autonomous Driving—0
Privacy Protection in Personalized Diffusion Models via Targeted Cross-Attention Adversarial Attack—0
Scaling Laws for Black box Adversarial Attacks—0
Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation—0
Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense—0
NMT-Obfuscator Attack: Ignore a sentence in translation with only one wordCode0
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning—0
BEARD: Benchmarking the Adversarial Robustness for Dataset DistillationCode0
Robust Optimal Power Flow Against Adversarial Attacks: A Tri-Level Optimization Approach—0
Chain Association-based Attacking and Shielding Natural Language Processing Systems—0
Neural Fingerprints for Adversarial Attack DetectionCode0
Attention Masks Help Adversarial Attacks to Bypass Safety DetectorsCode0
Seeing is Deceiving: Exploitation of Visual Pathways in Multi-Modal Language Models—0
Query-Efficient Adversarial Attack Against Vertical Federated Graph LearningCode0
LiDAttack: Robust Black-box Attack on LiDAR-based Object DetectionCode0
Replace-then-Perturb: Targeted Adversarial Attacks With Visual Reasoning for Vision-Language Models—0
Pseudo-Conversation Injection for LLM Goal Hijacking—0
Keep on Swimming: Real Attackers Only Need Partial Knowledge of a Multi-Model System—0
Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers—0
Evaluating the Robustness of LiDAR Point Cloud Tracking Against Adversarial Attack—0
Generative Adversarial Patches for Physical Attacks on Cross-Modal Pedestrian Re-Identification—0
Adversarial Attacks on Large Language Models Using Regularized RelaxationCode0
Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing—0
Toward Robust RALMs: Revealing the Impact of Imperfect Retrieval on Retrieval-Augmented Language ModelsCode0
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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