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Adversarial Purification

A class of adversarial defense methods that remove adversarial perturbations using a generative model.

Papers

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Showing 1–50 of 65 papers

TitleStatusHype
DiffCAP: Diffusion-based Cumulative Adversarial Purification for Vision Language Models—0
Fighting Fire with Fire (F3): A Training-free and Efficient Visual Adversarial Example Purification Method in LVLMs—0
How Do Diffusion Models Improve Adversarial Robustness?—0
Towards more transferable adversarial attack in black-box manner—0
FlowPure: Continuous Normalizing Flows for Adversarial PurificationCode1
Diffusion-based Adversarial Purification from the Perspective of the Frequency Domain—0
Defending Against Frequency-Based Attacks with Diffusion Models—0
LISArD: Learning Image Similarity to Defend Against Gray-box Adversarial AttacksCode0
Model-Free Adversarial Purification via Coarse-To-Fine Tensor Network Representation—0
VideoPure: Diffusion-based Adversarial Purification for Video RecognitionCode0
Gradient-Free Adversarial Purification with Diffusion Models—0
Divide and Conquer: Heterogeneous Noise Integration for Diffusion-based Adversarial Purification—0
Adversarial Purification by Consistency-aware Latent Space Optimization on Data Manifolds—0
Pre-trained Multiple Latent Variable Generative Models are good defenders against Adversarial AttacksCode0
Random Sampling for Diffusion-based Adversarial PurificationCode0
Adversarial Attacks and Robust Defenses in Speaker Embedding based Zero-Shot Text-to-Speech System—0
Improving Adversarial Robustness for 3D Point Cloud Recognition at Test-Time through Purified Self-Training—0
Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion ModelsCode0
LoRID: Low-Rank Iterative Diffusion for Adversarial Purification—0
High-Frequency Anti-DreamBooth: Robust Defense against Personalized Image SynthesisCode0
Instant Adversarial Purification with Adversarial Consistency Distillation—0
MsMemoryGAN: A Multi-scale Memory GAN for Palm-vein Adversarial Purification—0
Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information—0
Adversarially Robust Industrial Anomaly Detection Through Diffusion Model—0
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality MetricsCode1
ADBM: Adversarial diffusion bridge model for reliable adversarial purification—0
Diffusion-based Adversarial Purification for Intrusion DetectionCode0
ZeroPur: Succinct Training-Free Adversarial Purification—0
Large Language Model Sentinel: LLM Agent for Adversarial Purification—0
Towards Understanding the Robustness of Diffusion-Based Purification: A Stochastic Perspective—0
Adversarial purification for no-reference image-quality metrics: applicability study and new methods—0
Robust Diffusion Models for Adversarial Purification—0
Robust Overfitting Does Matter: Test-Time Adversarial Purification With FGSMCode0
PCLD: Point Cloud Layerwise Diffusion for Adversarial PurificationCode0
Adversarial Purification and Fine-tuning for Robust UDC Image Restoration—0
Adversarial Text Purification: A Large Language Model Approach for Defense—0
Adversarial Training on Purification (AToP): Advancing Both Robustness and GeneralizationCode1
PuriDefense: Randomized Local Implicit Adversarial Purification for Defending Black-box Query-based Attacks—0
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion AttacksCode1
MimicDiffusion: Purifying Adversarial Perturbation via Mimicking Clean Diffusion Model—0
Adversarial Purification of Information MaskingCode0
Diffusion models meet image counter-forensicsCode1
Adversarial Purification for Data-Driven Power System Event Classifiers with Diffusion Models—0
Purify++: Improving Diffusion-Purification with Advanced Diffusion Models and Control of Randomness—0
DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial PurificationCode1
Diffusion-Based Adversarial Purification for Speaker Verification—0
Language Guided Adversarial PurificationCode0
Advancing Adversarial Robustness Through Adversarial Logit Update—0
DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local SmoothingCode0
Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion ModelsCode1
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