Adversarial Defense
179 papers with code • 10 benchmarks • 5 datasets
Competitions with currently unpublished results:
Benchmarks
These leaderboards are used to track progress in Adversarial Defense
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Libraries
Use these libraries to find Adversarial Defense models and implementations
CIFAR-10
ImageNet
CIFAR-100
MNIST
Icons-50
Latest papers with no code
Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval
Furthermore, CA prevents the consequential model collapse, based on a novel metric, collapseness, which is incorporated into the optimization of perturbation.
Efficient Key-Based Adversarial Defense for ImageNet by Using Pre-trained Model
In this paper, we propose key-based defense model proliferation by leveraging pre-trained models and utilizing recent efficient fine-tuning techniques on ImageNet-1k classification.
MAD: Meta Adversarial Defense Benchmark
In addition, we introduce a meta-learning based adversarial training (Meta-AT) algorithm as the baseline, which features high robustness to unseen adversarial attacks through few-shot learning.
Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense
We aim to provide a general framework of for computational photography that recovers the real scene from imperfect images, via the Deep Nonparametric Convexified Filtering (DNCF).
Robust Adversarial Defense by Tensor Factorization
This study underscores the potential of integrating tensorization and low-rank decomposition as a robust defense against adversarial attacks in machine learning.
AdvFAS: A robust face anti-spoofing framework against adversarial examples
Ensuring the reliability of face recognition systems against presentation attacks necessitates the deployment of face anti-spoofing techniques.
Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models
After that, a universal adversarial purification framework is developed using the forward and reverse process of the pre-trained diffusion models to purify the perturbations from adversarial samples.
ATWM: Defense against adversarial malware based on adversarial training
In order to defend against malware attacks, researchers have proposed many Windows malware detection models based on deep learning.
DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks
In this paper, we propose DIFFender, a novel defense method that leverages a text-guided diffusion model to defend against adversarial patches.
Revisiting and Advancing Adversarial Training Through A Simple Baseline
In this paper, we delve into the essential components of adversarial training which is a pioneering defense technique against adversarial attacks.
