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 176–200 of 1808 papers

TitleStatusHype
Hiding Faces in Plain Sight: Defending DeepFakes by Disrupting Face DetectionCode1
Intermediate Outputs Are More Sensitive Than You Think—0
Fall Leaf Adversarial Attack on Traffic Sign Classification—0
Visual Adversarial Attack on Vision-Language Models for Autonomous Driving—0
Scaling Laws for Black box Adversarial Attacks—0
Privacy Protection in Personalized Diffusion Models via Targeted Cross-Attention Adversarial Attack—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
Seeing is Deceiving: Exploitation of Visual Pathways in Multi-Modal Language Models—0
Attention Masks Help Adversarial Attacks to Bypass Safety DetectorsCode0
Neural Fingerprints for Adversarial Attack DetectionCode0
Query-Efficient Adversarial Attack Against Vertical Federated Graph LearningCode0
Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language AttackCode1
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
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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