Adversarial Attack
597 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 with no code
Double Privacy Guard: Robust Traceable Adversarial Watermarking against Face Recognition
This strategy enhances the representation of universal carrier features, mitigating multi-objective optimization conflicts in watermarking.
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives
Meanwhile, it is important to note that the correlation, like ranking correlation, plays a significant role in NR-IQA tasks.
AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation
Specifically, our approach identifies the Principal Adversarial Domains (PADs), i. e., a combination of features of the adversarial examples from different attacks, which possesses large coverage of the entire adversarial feature space.
Towards a Novel Perspective on Adversarial Examples Driven by Frequency
In this paper, we seek to demystify this relationship by exploring the characteristics of adversarial perturbations within the frequency domain.
Adversarial Identity Injection for Semantic Face Image Synthesis
Among all the explored techniques, Semantic Image Synthesis (SIS) methods, whose goal is to generate an image conditioned on a semantic segmentation mask, are the most promising, even though preserving the perceived identity of the input subject is not their main concern.
Towards Building a Robust Toxicity Predictor
Recent NLP literature pays little attention to the robustness of toxicity language predictors, while these systems are most likely to be used in adversarial contexts.
BruSLeAttack: A Query-Efficient Score-Based Black-Box Sparse Adversarial Attack
We study the unique, less-well understood problem of generating sparse adversarial samples simply by observing the score-based replies to model queries.
Adversarial Attacks and Dimensionality in Text Classifiers
For all of the aforementioned studies, we have run tests on multiple models with varying dimensionality and used a word-vector level adversarial attack to substantiate the findings.
ADVREPAIR:Provable Repair of Adversarial Attack
Existing neuron-level methods using limited data lack efficacy in fixing adversaries due to the inherent complexity of adversarial attack mechanisms, while adversarial training, leveraging a large number of adversarial samples to enhance robustness, lacks provability.
Multi-granular Adversarial Attacks against Black-box Neural Ranking Models
However, limiting perturbations to a single level of granularity may reduce the flexibility of adversarial examples, thereby diminishing the potential threat of the attack.
