Xiaojun Jia | DeepAI

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∙ 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...

0 Xiaojun Jia, et al. ∙

∙ 07/24/2023

Robust Automatic Speech Recognition via WavAugment Guided Phoneme Adversarial Training

Developing a practically-robust automatic speech recognition (ASR) is ch...

0 Gege Qi, et al. ∙

∙ 04/01/2023

Improving Fast Adversarial Training with Prior-Guided Knowledge

Fast adversarial training (FAT) is an efficient method to improve robust...

0 Xiaojun Jia, et al. ∙

∙ 11/29/2022

Context-Aware Robust Fine-Tuning

Contrastive Language-Image Pre-trained (CLIP) models have zero-shot abil...

0 Xiaofeng Mao, et al. ∙

∙ 09/16/2022

A Large-scale Multiple-objective Method for Black-box Attack against Object Detection

Recent studies have shown that detectors based on deep models are vulner...

0 Siyuan Liang, et al. ∙

∙ 08/04/2022

MOVE: Effective and Harmless Ownership Verification via Embedded External Features

Currently, deep neural networks (DNNs) are widely adopted in different a...

0 Yiming Li, et al. ∙

∙ 07/18/2022

Prior-Guided Adversarial Initialization for Fast Adversarial Training

Fast adversarial training (FAT) effectively improves the efficiency of s...

0 Xiaojun Jia, et al. ∙

∙ 07/17/2022

Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal

As a common security tool, visible watermarking has been widely applied ...

12 Xinwei Liu, et al. ∙

∙ 03/13/2022

LAS-AT: Adversarial Training with Learnable Attack Strategy

Adversarial training (AT) is always formulated as a minimax problem, of ...

0 Xiaojun Jia, et al. ∙

∙ 12/07/2021

Defending against Model Stealing via Verifying Embedded External Features

Obtaining a well-trained model involves expensive data collection and tr...

0 Yiming Li, et al. ∙

∙ 10/11/2021

Boosting Fast Adversarial Training with Learnable Adversarial Initialization

Adversarial training (AT) has been demonstrated to be effective in impro...

7 Xiaojun Jia, et al. ∙

∙ 08/01/2021

An Effective and Robust Detector for Logo Detection

In recent years, intellectual property (IP), which represents literary, ...

0 Xiaojun Jia, et al. ∙

∙ 08/05/2020

Adv-watermark: A Novel Watermark Perturbation for Adversarial Examples

Recent research has demonstrated that adding some imperceptible perturba...

0 Xiaojun Jia, et al. ∙

∙ 09/11/2019

Identifying and Resisting Adversarial Videos Using Temporal Consistency

Video classification is a challenging task in computer vision. Although ...

0 Xiaojun Jia, et al. ∙

∙ 11/30/2018

ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples

Deep neural networks (DNNs) have been demonstrated to be vulnerable to a...

0 Xiaojun Jia, et al. ∙

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