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

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Showing 51–100 of 1808 papers

TitleStatusHype
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation ModelsCode1
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic SegmentationCode1
Benchmarking Adversarial Robustness on Image ClassificationCode1
BERT-ATTACK: Adversarial Attack Against BERT Using BERTCode1
Boosting the Adversarial Transferability of Surrogate Models with Dark KnowledgeCode1
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial PerturbationCode1
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot NavigationCode1
To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning ModelsCode1
CgAT: Center-Guided Adversarial Training for Deep Hashing-Based RetrievalCode1
Certifying LLM Safety against Adversarial PromptingCode1
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted AttackCode1
On the Multi-modal Vulnerability of Diffusion ModelsCode1
An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacksCode1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical FlowCode1
An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat LandscapeCode1
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency DomainCode1
An Efficient Adversarial Attack for Tree EnsemblesCode1
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
Audio Jailbreak Attacks: Exposing Vulnerabilities in SpeechGPT in a White-Box FrameworkCode1
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing FlowsCode1
Adversarial Attack On Yolov5 For Traffic And Road Sign DetectionCode1
Adversarial Vulnerability of Randomized EnsemblesCode1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Adversarial Self-Supervised Contrastive LearningCode1
Fooling the Image Dehazing Models by First Order GradientCode1
Adversarial Attack and Defense in Deep RankingCode1
Adversarial Attack and Defense of Structured Prediction ModelsCode1
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving ScenariosCode1
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality MetricsCode1
Adversarial Training for Free!Code1
Adversarial Attack and Defense Strategies for Deep Speaker Recognition SystemsCode1
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial TransferabilityCode1
3D Gaussian Splat VulnerabilitiesCode1
An Extensive Study on Adversarial Attack against Pre-trained Models of CodeCode1
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV TrackingCode1
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural NetworksCode1
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-ArtCode1
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and BeyondCode1
Are AlphaZero-like Agents Robust to Adversarial Perturbations?Code1
A Review of Adversarial Attack and Defense for Classification MethodsCode1
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query AttacksCode1
Adversarial Attack on Community Detection by Hiding IndividualsCode1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNsCode1
Adversarial Attack on Deep Learning-Based Splice LocalizationCode1
Data-free Universal Adversarial Perturbation with Pseudo-semantic PriorCode1
3D Adversarial Attacks Beyond Point CloudCode1
Adversarial Attack on Graph Neural Networks as An Influence Maximization ProblemCode1
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language ModelsCode1
CausalAdv: Adversarial Robustness through the Lens of CausalityCode1
Adversarial Vulnerabilities in Large Language Models for Time Series ForecastingCode1
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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