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Found 14 papers, 3 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.
Robust Backdoor Attacks on Object Detection in Real World
However, these models are vulnerable to backdoor attacks.
LEA2: A Lightweight Ensemble Adversarial Attack via Non-overlapping Vulnerable Frequency Regions
However, most existing ensemble attacks depend on numerous substitute models to cover the vulnerable subspace of a target model.
Towards the Desirable Decision Boundary by Moderate-Margin Adversarial Training
Adversarial training, as one of the most effective defense methods against adversarial attacks, tends to learn an inclusive decision boundary to increase the robustness of deep learning models.
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.
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art
Then, we conduct a comprehensive and systematic review to categorize the state-of-the-art adversarial attacks against PE malware detection, as well as corresponding defenses to increase the robustness of Windows PE malware detection.
Edge-aware Guidance Fusion Network for RGB Thermal Scene Parsing
Considering the importance of high level semantic information, we propose a global information module and a semantic information module to extract rich semantic information from the high-level features.
Ranked #11 on
Thermal Image Segmentation
on PST900
RNAS: Robust Network Architecture Search beyond DARTS
The vulnerability of Deep Neural Networks (DNNs) (i. e., susceptibility to adversarial attacks) severely limits the application of DNNs.
Versailles-FP dataset: Wall Detection in Ancient
Since the first step in the building's or monument's 3D model is the wall detection in the floor plan, we introduce in this paper the new and unique Versailles FP dataset of wall groundtruthed images of the Versailles Palace dated between 17th and 18th century.
Person Re-identification based on Robust Features in Open-world
At the same time, to verify the effectiveness of our method, we provide a miniature dataset which is closer to the real world and includes pedestrian changing clothes and cross-modality factor variables fusion.
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.
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.
