Defending Adversarial Examples by Negative Correlation Ensemble
The security issues in DNNs, such as adversarial examples, have attracted much attention. Adversarial examples refer to the examples which are capable to induce the DNNs return completely predictions by introducing carefully designed perturbations. Obviously, adversarial examples bring great security risks to the development of deep learning. Recently, Some defense approaches against adversarial examples have been proposed, however, in our opinion, the performance of these approaches are still limited. In this paper, we propose a new ensemble defense approach named the Negative Correlation Ensemble (NCEn), which achieves compelling results by introducing gradient directions and gradient magnitudes of each member in the ensemble negatively correlated and at the same time, reducing the transferability of adversarial examples among them. Extensive experiments have been conducted, and the results demonstrate that NCEn can improve the adversarial robustness of ensembles effectively.
Code (1)
Tasks
Adversarial RobustnessSimilar Papers 제목 키워드 기반
$n$-ML: Mitigating Adversarial Examples via Ensembles of Topologically Manipulated Classifiers
This paper proposes a new defense called $n$-ML against adversarial examples, i.e., inputs crafted by perturbing benign inputs by small amounts to induce misclassifications by classifiers. Inspired by $n$-version program…
General ClassificationExploring Model Learning Heterogeneity for Boosting Ensemble Robustness
Deep neural network ensembles hold the potential of improving generalization performance for complex learning tasks. This paper presents formal analysis and empirical evaluation to show that heterogeneous deep ensembles …
Diversityobject-detectionObject DetectionSemantic SegmentationStochastic Combinatorial Ensembles for Defending Against Adversarial Examples
Many deep learning algorithms can be easily fooled with simple adversarial examples. To address the limitations of existing defenses, we devised a probabilistic framework that can generate an exponentially large ensemble…
Adversarial AttackMetric LearningDefending against Machine Learning based Inference Attacks via Adversarial Examples: Opportunities and Challenges
As machine learning (ML) becomes more and more powerful and easily accessible, attackers increasingly leverage ML to perform automated large-scale inference attacks in various domains. In such an ML-equipped inference at…
BIG-bench Machine LearningInference AttackA Simple General Method for Detecting Textual Adversarial Examples
Although deep neural networks have achieved state-of-the-art performance in various machine learning and artificial intelligence tasks, adversarial examples, constructed by adding small non-random perturbations to correc…
Ensemble LearningRepresentation LearningSentence