Search Results for author:
Found 18 papers, 4 papers with code
F$^2$AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns
We propose a Feature-Focusing Adversarial Training (F$^2$AT), which differs from previous work in that it enforces the model to focus on the core features from natural patterns and reduce the impact of spurious features from perturbed patterns.
When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection
In this paper, we focus on the Positive and Unlabeled (PU) learning problem for vulnerability detection and propose a novel model named PILOT, i. e., PositIve and unlabeled Learning mOdel for vulnerability deTection.
Adversarial Attacks on ASR Systems: An Overview
With the development of hardware and algorithms, ASR(Automatic Speech Recognition) systems evolve a lot.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Robust Network Architecture Search via Feature Distortion Restraining
This process is modeled as a multi-objective bilevel optimization problem and a novel algorithm is proposed to solve this optimization.
Hessian-Free Second-Order Adversarial Examples for Adversarial Learning
Extensive experiments conducted on the MINIST and CIFAR-10 datasets show that our adversarial learning with second-order adversarial examples outperforms other fisrt-order methods, which can improve the model robustness against a wide range of attacks.
Improving robustness of language models from a geometry-aware perspective
On top of FADA, we propose geometry-aware adversarial training (GAT) to perform adversarial training on friendly adversarial data so that we can save a large number of search steps.
One model Packs Thousands of Items with Recurrent Conditional Query Learning
In this paper, we propose a Recurrent Conditional Query Learning (RCQL) method to solve both 2D and 3D packing problems.
TREATED:Towards Universal Defense against Textual Adversarial Attacks
Recent work shows that deep neural networks are vulnerable to adversarial examples.
CODEs: Chamfer Out-of-Distribution Examples against Overconfidence Issue
Overconfident predictions on out-of-distribution (OOD) samples is a thorny issue for deep neural networks.
Generative Adversarial Network
Out of Distribution (OOD) Detection
Towards Speeding up Adversarial Training in Latent Spaces
Adversarial training is wildly considered as one of the most effective way to defend against adversarial examples.
Category Disentangled Context: Turning Category-irrelevant Features Into Treasures
Deep neural networks have achieved great success in computer vision, thanks to their ability in extracting category-relevant semantic features.
An Adversarial Attack via Feature Contributive Regions
Recently, to deal with the vulnerability to generate examples of CNNs, there are many advanced algorithms that have been proposed.
Visually Imperceptible Adversarial Patch Attacks on Digital Images
With this soft mask, we develop a new loss function with inverse temperature to search for optimal perturbations in CFR.
EI-MTD:Moving Target Defense for Edge Intelligence against Adversarial Attacks
This dynamic defense can prohibit the adversary from selecting an optimal substitute model for black-box attacks.
TEAM: We Need More Powerful Adversarial Examples for DNNs
In this paper, we propose a novel method (TEAM, Taylor Expansion-Based Adversarial Methods) to generate more powerful adversarial examples than previous methods.
Decision Propagation Networks for Image Classification
High-level (e. g., semantic) features encoded in the latter layers of convolutional neural networks are extensively exploited for image classification, leaving low-level (e. g., color) features in the early layers underexplored.
Solving Packing Problems by Conditional Query Learning
Previous studies have shown that NCO outperforms heuristic algorithms in many combinatorial optimization problems such as the routing problems.
Attending Category Disentangled Global Context for Image Classification
In this paper, we propose a general framework for image classification using the attention mechanism and global context, which could incorporate with various network architectures to improve their performance.
