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Found 10 papers, 1 papers with code
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.
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.
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.
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.
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.
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.
Towards a Neural Model for Serial Order in Frontal Cortex: a Brain Theory from Memory Development to Higher-Level Cognition
We propose that the immature prefrontal cortex (PFC) use its primary functionality of detecting hierarchical patterns in temporal signals as a second purpose to organize the spatial ordering of the cortical networks in the developing brain itself.
TEAM: An Taylor Expansion-Based Method for Generating Adversarial Examples
Although Deep Neural Networks(DNNs) have achieved successful applications in many fields, they are vulnerable to adversarial examples. Adversarial training is one of the most effective methods to improve the robustness of DNNs, and it is generally considered as solving a saddle point problem that minimizes risk and maximizes perturbation. Therefore, powerful adversarial examples can effectively replicate the situation of perturbation maximization to solve the saddle point problem. The method proposed in this paper approximates the output of DNNs in the input neighborhood by using the Taylor expansion, and then optimizes it by using the Lagrange multiplier method to generate adversarial examples.
A Unified Multilingual Handwriting Recognition System using multigrams sub-lexical units
This makes pos- sible the design of an end-to-end unified multilingual recognition system where both a single optical model and a single language model are trained on all the languages.
A syllable based model for handwriting recognition
In this paper, we introduce a new modeling approach of texts for handwriting recognition based on syllables.
