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 1276–1300 of 1808 papers

TitleStatusHype
I2VGuard: Safeguarding Images against Misuse in Diffusion-based Image-to-Video Models—0
Identification of Attack-Specific Signatures in Adversarial Examples—0
Identification of Systematic Errors of Image Classifiers on Rare Subgroups—0
Identifying Classes Susceptible to Adversarial Attacks—0
Identifying Informative Latent Variables Learned by GIN via Mutual Information—0
IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection—0
IDT: Dual-Task Adversarial Attacks for Privacy Protection—0
ILFO: Adversarial Attack on Adaptive Neural Networks—0
Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks—0
Image-based Multimodal Models as Intruders: Transferable Multimodal Attacks on Video-based MLLMs—0
ImF: Implicit Fingerprint for Large Language Models—0
Impact of Scaled Image on Robustness of Deep Neural Networks—0
Imperceptible Adversarial Attack on Deep Neural Networks from Image Boundary—0
Imperceptible CMOS camera dazzle for adversarial attacks on deep neural networks—0
Imperceptible Physical Attack against Face Recognition Systems via LED Illumination Modulation—0
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability—0
Improved Adversarial Training via Learned Optimizer—0
Improving adversarial robustness of deep neural networks by using semantic information—0
Enhancing Transferability of Adversarial Examples with Spatial Momentum—0
Improving Adversarial Transferability with Scheduled Step Size and Dual Example—0
Improving Deep Learning Model Robustness Against Adversarial Attack by Increasing the Network Capacity—0
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder—0
Improving Network Interpretability via Explanation Consistency Evaluation—0
Improving Neural Network Robustness through Neighborhood Preserving Layers—0
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator—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