research
∙
08/22/2023
Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging
Fast Adversarial Training (FAT) not only improves the model robustness b...
share
research
∙
07/24/2023
COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts
Practical object detection application can lose its effectiveness on ima...
research
∙
07/24/2023
Robust Automatic Speech Recognition via WavAugment Guided Phoneme Adversarial Training
Developing a practically-robust automatic speech recognition (ASR) is ch...
research
∙
03/30/2023
ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing
Recent studies have shown that higher accuracy on ImageNet usually leads...
research
∙
03/28/2023
TransAudio: Towards the Transferable Adversarial Audio Attack via Learning Contextualized Perturbations
In a transfer-based attack against Automatic Speech Recognition (ASR) sy...
research
∙
03/21/2023
Information-containing Adversarial Perturbation for Combating Facial Manipulation Systems
With the development of deep learning technology, the facial manipulatio...
research
∙
11/30/2022
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective
Out-Of-Distribution (OOD) detection has received broad attention over th...
research
∙
11/29/2022
Context-Aware Robust Fine-Tuning
Contrastive Language-Image Pre-trained (CLIP) models have zero-shot abil...
research
∙
10/17/2022
Defects of Convolutional Decoder Networks in Frequency Representation
In this paper, we prove representation bottlenecks of a cascaded convolu...
research
∙
10/13/2022
Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition
Due to the absence of connectives, implicit discourse relation recogniti...
research
∙
10/09/2022
Boosting Out-of-distribution Detection with Typical Features
Out-of-distribution (OOD) detection is a critical task for ensuring the ...
research
∙
09/16/2022
Enhance the Visual Representation via Discrete Adversarial Training
Adversarial Training (AT), which is commonly accepted as one of the most...
research
∙
10/15/2021
Adversarial Attacks on ML Defense Models Competition
Due to the vulnerability of deep neural networks (DNNs) to adversarial e...
research
∙
08/20/2021
AdvDrop: Adversarial Attack to DNNs by Dropping Information
Human can easily recognize visual objects with lost information: even lo...
research
∙
05/17/2021
Rethinking the Design Principles of Robust Vision Transformer
Recent advances on Vision Transformers (ViT) have shown that self-attent...
research
∙
03/11/2021
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink
Though it is well known that the performance of deep neural networks (DN...
research
∙
03/04/2021
QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval
We study the query-based attack against image retrieval to evaluate its ...
research
∙
03/02/2021
Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain
The remarkable success in face forgery techniques has received considera...
research
∙
02/23/2021
Adversarial Examples Detection beyond Image Space
Deep neural networks have been proved that they are vulnerable to advers...
research
∙
12/10/2020
Composite Adversarial Attacks
Adversarial attack is a technique for deceiving Machine Learning (ML) mo...
research
∙
08/11/2020
Sharp Multiple Instance Learning for DeepFake Video Detection
With the rapid development of facial manipulation techniques, face forge...
research
∙
06/09/2020
GAP++: Learning to generate target-conditioned adversarial examples
Adversarial examples are perturbed inputs which can cause a serious thre...
research
∙
11/15/2019
AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate Gradients
Deep neural networks have been shown to be vulnerable to adversarial exa...
research
∙
11/15/2019
Learning To Characterize Adversarial Subspaces
Deep Neural Networks (DNNs) are known to be vulnerable to the maliciousl...
research
∙
11/15/2019
Self-supervised Adversarial Training
Recent work has demonstrated that neural networks are vulnerable to adve...
research
∙
11/14/2019
Self-Supervised Learning For Few-Shot Image Classification
Few-shot image classification aims to classify unseen classes with limit...
research
∙
03/19/2019
Robust Visual Tracking Using Dynamic Classifier Selection with Sparse Representation of Label Noise
Recently a category of tracking methods based on "tracking-by-detection"...
research
∙
03/18/2019