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 801–850 of 1808 papers

TitleStatusHype
Towards Sybil Resilience in Decentralized Learning—0
Cross-lingual Cross-temporal Summarization: Dataset, Models, EvaluationCode0
Adversarial Attacks Neutralization via Data Set Randomization—0
Physics-constrained Attack against Convolution-based Human Motion PredictionCode0
Sample Attackability in Natural Language Adversarial AttacksCode0
You Don't Need Robust Machine Learning to Manage Adversarial Attack Risks—0
A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models—0
Malafide: a novel adversarial convolutive noise attack against deepfake and spoofing detection systemsCode0
I See Dead People: Gray-Box Adversarial Attack on Image-To-Text Models—0
Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions—0
COVER: A Heuristic Greedy Adversarial Attack on Prompt-based Learning in Language Models—0
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers—0
Expanding Scope: Adapting English Adversarial Attacks to ChineseCode0
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning—0
A Robust Likelihood Model for Novelty Detection—0
Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering ApproachCode0
KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language ExplanationsCode0
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception—0
Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy—0
Adversarial Attack Based on Prediction-Correction—0
Adversarial-Aware Deep Learning System based on a Secondary Classical Machine Learning Verification Approach—0
Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search—0
Graph-based methods coupled with specific distributional distances for adversarial attack detectionCode0
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation FrameworkCode0
Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision MakingCode0
PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation—0
Another Dead End for Morphological Tags? Perturbed Inputs and ParsingCode0
Enhancing Accuracy and Robustness through Adversarial Training in Class Incremental Continual Learning—0
Latent Magic: An Investigation into Adversarial Examples Crafted in the Semantic Latent Space—0
Attribute-Guided Encryption with Facial Texture Masking—0
Are Your Explanations Reliable? Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial AttackCode0
Dynamic Transformers Provide a False Sense of EfficiencyCode0
Spatial-Frequency Discriminability for Revealing Adversarial PerturbationsCode0
Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend—0
Content-based Unrestricted Adversarial Attack—0
Iterative Adversarial Attack on Image-guided Story Ending Generation—0
Attacking Perceptual Similarity Metrics—0
A Black-Box Attack on Code Models via Representation Nearest Neighbor Search—0
The Best Defense is Attack: Repairing Semantics in Textual Adversarial Examples—0
New Adversarial Image Detection Based on Sentiment AnalysisCode0
Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed FeatureCode0
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples—0
Evaluating Adversarial Robustness on Document Image Classification—0
Wavelets Beat Monkeys at Adversarial Robustness—0
Towards the Transferable Audio Adversarial Attack via Ensemble Methods—0
Combining Generators of Adversarial Malware Examples to Increase Evasion RateCode0
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense—0
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection—0
Fast Adversarial CNN-based Perturbation Attack of No-Reference Image Quality MetricsCode0
Generating Adversarial Attacks in the Latent Space—0
Show:102550
← PrevPage 17 of 37Next →
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