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Latest papers without code

Evaluating Deception Detection Model Robustness To Linguistic Variation

23 Apr 2021

With the increasing use of machine-learning driven algorithmic judgements, it is critical to develop models that are robust to evolving or manipulated inputs.

ADVERSARIAL DEFENSE DECEPTION DETECTION MISINFORMATION

Improved Autoregressive Modeling with Distribution Smoothing

ICLR 2021

While autoregressive models excel at image compression, their sample quality is often lacking.

ADVERSARIAL DEFENSE IMAGE COMPRESSION

Learning Defense Transformers for Counterattacking Adversarial Examples

13 Mar 2021

Relying on this, we learn a defense transformer to counterattack the adversarial examples by parameterizing the affine transformations and exploiting the boundary information of DNNs.

ADVERSARIAL DEFENSE

Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN

8 Mar 2021

In order to defend against the adversarial perturbations, adversarially trained GAN (ATGAN) is proposed to improve the adversarial robustness generalization of the state-of-the-art CNNs trained by adversarial training.

ADVERSARIAL DEFENSE DATA AUGMENTATION IMAGE CLASSIFICATION

Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids

17 Feb 2021

False data injection attack (FDIA) is a critical security issue in power system state estimation.

ADVERSARIAL DEFENSE

Robust Android Malware Detection System against Adversarial Attacks using Q-Learning

27 Jan 2021

Finally, we propose an adversarial defense strategy that reduces the average fooling rate by threefold to 15. 22% against a single policy attack, thereby increasing the robustness of the detection models i. e. the proposed model can effectively detect variants (metamorphic) of malware.

ADVERSARIAL DEFENSE ANDROID MALWARE DETECTION MALWARE DETECTION Q-LEARNING

A Comprehensive Evaluation Framework for Deep Model Robustness

24 Jan 2021

Through neuron coverage and data imperceptibility, we use data-oriented metrics to measure the integrity of test examples; by delving into model structure and behavior, we exploit model-oriented metrics to further evaluate robustness in the adversarial setting.

ADVERSARIAL DEFENSE