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Adversarial Purification

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

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Showing 1–25 of 65 papers

TitleStatusHype
Diffusion Models for Adversarial PurificationCode2
Adversarial Training on Purification (AToP): Advancing Both Robustness and GeneralizationCode1
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality MetricsCode1
DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial PurificationCode1
Defending against Adversarial Audio via Diffusion ModelCode1
Diffusion models meet image counter-forensicsCode1
FlowPure: Continuous Normalizing Flows for Adversarial PurificationCode1
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion AttacksCode1
Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based ModelsCode1
Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion ModelsCode1
Guided Diffusion Model for Adversarial PurificationCode1
Adversarial purification with Score-based generative modelsCode1
Robust Evaluation of Diffusion-Based Adversarial PurificationCode1
Adversarial Text Purification: A Large Language Model Approach for Defense—0
Adversarial Purification and Fine-tuning for Robust UDC Image Restoration—0
Adversarially Robust Industrial Anomaly Detection Through Diffusion Model—0
ADBM: Adversarial diffusion bridge model for reliable adversarial purification—0
Divide and Conquer: Heterogeneous Noise Integration for Diffusion-based Adversarial Purification—0
DiffCAP: Diffusion-based Cumulative Adversarial Purification for Vision Language Models—0
Diffusion-based Adversarial Purification from the Perspective of the Frequency Domain—0
Adversarial purification for no-reference image-quality metrics: applicability study and new methods—0
Adversarial Attacks and Robust Defenses in Speaker Embedding based Zero-Shot Text-to-Speech System—0
Diffusion-Based Adversarial Purification for Speaker Verification—0
Adversarial Purification through Representation Disentanglement—0
Advancing Adversarial Robustness Through Adversarial Logit Update—0
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