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 901–925 of 1808 papers

TitleStatusHype
Practical Order Attack in Deep Ranking—0
PRAT: PRofiling Adversarial aTtacks—0
Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Algorithm—0
Preventing Non-intrusive Load Monitoring Privacy Invasion: A Precise Adversarial Attack Scheme for Networked Smart Meters—0
Prior Networks for Detection of Adversarial Attacks—0
Privacy Protection in Personalized Diffusion Models via Targeted Cross-Attention Adversarial Attack—0
Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage—0
Probabilistic Categorical Adversarial Attack & Adversarial Training—0
Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection—0
Probing Model Signal-Awareness via Prediction-Preserving Input Minimization—0
Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach—0
ProjAttacker: A Configurable Physical Adversarial Attack for Face Recognition via Projector—0
Prompt2Perturb (P2P): Text-Guided Diffusion-Based Adversarial Attack on Breast Ultrasound Images—0
Prompt-driven Transferable Adversarial Attack on Person Re-Identification with Attribute-aware Textual Inversion—0
Propagated Perturbation of Adversarial Attack for well-known CNNs: Empirical Study and its Explanation—0
PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks—0
Protection against Cloning for Deep Learning—0
Protego: Detecting Adversarial Examples for Vision Transformers via Intrinsic Capabilities—0
Protein Folding Neural Networks Are Not Robust—0
Pseudo-Conversation Injection for LLM Goal Hijacking—0
Q-FAKER: Query-free Hard Black-box Attack via Controlled Generation—0
QFAL: Quantum Federated Adversarial Learning—0
Query-Efficient Black-Box Attack by Active Learning—0
Query-Efficient Hard-Label Black-Box Attack against Vision Transformers—0
Query-Efficient Video Adversarial Attack with Stylized Logo—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