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 1226–1250 of 1808 papers

TitleStatusHype
PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation—0
Pelta: Shielding Transformers to Mitigate Evasion Attacks in Federated Learning—0
Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception—0
Perception Improvement for Free: Exploring Imperceptible Black-box Adversarial Attacks on Image Classification—0
Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions—0
Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions—0
Watertox: The Art of Simplicity in Universal Attacks A Cross-Model Framework for Robust Adversarial Generation—0
Towards Sybil Resilience in Decentralized Learning—0
Adversarial Infrared Geometry: Using Geometry to Perform Adversarial Attack against Infrared Pedestrian Detectors—0
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions—0
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense—0
Towards the Transferable Audio Adversarial Attack via Ensemble Methods—0
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability—0
Adversarial Imitation Attack—0
Fooling the primate brain with minimal, targeted image manipulation—0
PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection—0
Phrase-level Textual Adversarial Attack with Label Preservation—0
Adversarial Identity Injection for Semantic Face Image Synthesis—0
Adversarial Fine-tune with Dynamically Regulated Adversary—0
Adversarial Exposure Attack on Diabetic Retinopathy Imagery Grading—0
Physical Adversarial Attack on Monocular Depth Estimation via Shape-Varying Patches—0
Physical Adversarial Attack on Vehicle Detector in the Carla Simulator—0
Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook—0
Adversarial Examples in Deep Learning: Characterization and Divergence—0
PICA: A Pixel Correlation-based Attentional Black-box Adversarial Attack—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