Adversarial Attack
598 papers with code • 2 benchmarks • 9 datasets
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
Libraries
Use these libraries to find Adversarial Attack models and implementationsDatasets
CIFAR-10
WSJ0-2mix
ImageNet-P
NAS-Bench-1Shot1
Cifar10Mnist
PointDenoisingBenchmark
REAP
Subtasks
Latest papers
Revealing Vulnerabilities in Stable Diffusion via Targeted Attacks
datar001/revealing-vulnerabilities-in-stable-diffusion-via-targeted-attacks
•
•
In this study, we formulate the problem of targeted adversarial attack on Stable Diffusion and propose a framework to generate adversarial prompts.
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model
With the functional and characteristic similarity analysis, we introduce a novel gradient editing (GE) mechanism and verify its feasibility in generating transferable samples on various models.
AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection
While DeepFake applications are becoming popular in recent years, their abuses pose a serious privacy threat.
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification
However, the present few-shot NER models assume that the labeled data are all clean without noise or outliers, and there are few works focusing on the robustness of the cross-domain transfer learning ability to textual adversarial attacks in Few-shot NER.
ScAR: Scaling Adversarial Robustness for LiDAR Object Detection
xiaohulugo/ScAR-Scaling-Adversarial-Robustness-for-LiDAR-Object-Detection
•
•
Universal adversarial attack methods such as Fast Sign Gradient Method (FSGM) and Projected Gradient Descend (PGD) are popular for LiDAR object detection, but they are often deficient compared to task-specific adversarial attacks.
Adversarial Purification of Information Masking
Notably, the residual perturbations on the purified image primarily stem from the same-position patch and similar patches of the adversarial sample.
Trainwreck: A damaging adversarial attack on image classifiers
Adversarial attacks are an important security concern for computer vision (CV), as they enable malicious attackers to reliably manipulate CV models.
An Extensive Study on Adversarial Attack against Pre-trained Models of Code
Although several approaches have been proposed to generate adversarial examples for PTMC, the effectiveness and efficiency of such approaches, especially on different code intelligence tasks, has not been well understood.
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selection
In this work, we explore the usage of an ensemble of deep learning models as our thief model.
Amoeba: Circumventing ML-supported Network Censorship via Adversarial Reinforcement Learning
mobile-intelligence-lab/amoeba
•
•
Specifically, we cast the problem of finding adversarial flows that will be misclassified as a sequence generation task, which we solve with Amoeba, a novel reinforcement learning algorithm that we design.
