84 citations · 375 across the 68 of their papers we have counts for
8 papers · 1 filter
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
Jianan Zhou, Jianing Zhu, Jingfeng Zhang +4
Adversarial training (AT) with imperfect supervision is significant but receives limited attention. To push AT towards more practical scenarios, we explore a brand new yet challeng…
FuncFooler: A Practical Black-box Attack Against Learning-based Binary Code Similarity Detection Methods
Lichen Jia, Bowen Tang, Chenggang Wu +6
The binary code similarity detection (BCSD) method measures the similarity of two binary executable codes. Recently, the learning-based BCSD methods have achieved great success, ou…
Accelerating Score-based Generative Models for High-Resolution Image Synthesis
Hengyuan Ma, Li Zhang, Xiatian Zhu +2
Score-based generative models (SGMs) have recently emerged as a promising class of generative models. The key idea is to produce high-quality images by recurrently adding Gaussian…
Diverse Instance Discovery: Vision-Transformer for Instance-Aware Multi-Label Image Recognition
Yunqing Hu, Xuan Jin, Yin Zhang +5
Previous works on multi-label image recognition (MLIR) usually use CNNs as a starting point for research. In this paper, we take pure Vision Transformer (ViT) as the research base…
WaveFuzz: A Clean-Label Poisoning Attack to Protect Your Voice
Yunjie Ge, Qian Wang, Jingfeng Zhang +3
People are not always receptive to their voice data being collected and misused. Training the audio intelligence systems needs these data to build useful features, but the cost for…
On the Effectiveness of Adversarial Training against Backdoor Attacks
Yinghua Gao, Dongxian Wu, Jingfeng Zhang +4
DNNs' demand for massive data forces practitioners to collect data from the Internet without careful check due to the unacceptable cost, which brings potential risks of backdoor at…
Adversarial Attack and Defense for Non-Parametric Two-Sample Tests
Xilie Xu, Jingfeng Zhang, Feng Liu +2
Non-parametric two-sample tests (TSTs) that judge whether two sets of samples are drawn from the same distribution, have been widely used in the analysis of critical data. People t…
Towards Adversarially Robust Deep Image Denoising
Hanshu Yan, Jingfeng Zhang, Jiashi Feng +2
This work systematically investigates the adversarial robustness of deep image denoisers (DIDs), i.e, how well DIDs can recover the ground truth from noisy observations degraded by…