7 citations · 21 across the 13 of their papers we have counts for
15 papers
Data-Free Universal Attack by Exploiting the Intrinsic Vulnerability of Deep Models
YangTian Yan, Jinyu Tian
Deep neural networks (DNNs) are susceptible to Universal Adversarial Perturbations (UAPs), which are instance agnostic perturbations that can deceive a target model across a wide r…
Anti-Diffusion: Preventing Abuse of Modifications of Diffusion-Based Models
Zheng Li, Liangbin Xie, Jiantao Zhou +3
Although diffusion-based techniques have shown remarkable success in image generation and editing tasks, their abuse can lead to severe negative social impacts. Recently, some work…
Deferred Poisoning: Making the Model More Vulnerable via Hessian Singularization
Yuhao He, Jinyu Tian, Xianwei Zheng +3
Recent studies have shown that deep learning models are very vulnerable to poisoning attacks. Many defense methods have been proposed to address this issue. However, traditional po…
DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency Domain
Fengpeng Li, Kemou Li, Haiwei Wu +2
To protect deep neural networks (DNNs) from adversarial attacks, adversarial training (AT) is developed by incorporating adversarial examples (AEs) into model training. Recent stud…
DifAttack++: Query-Efficient Black-Box Adversarial Attack via Hierarchical Disentangled Feature Space in Cross-Domain
Jun Liu, Jiantao Zhou, Jiandian Zeng +2
This work investigates efficient score-based black-box adversarial attacks that achieve a high Attack Success Rate (ASR) and good generalization ability. We propose a novel attack…
Regroup Median Loss for Combating Label Noise
Fengpeng Li, Kemou Li, Jinyu Tian +1
The deep model training procedure requires large-scale datasets of annotated data. Due to the difficulty of annotating a large number of samples, label noise caused by incorrect an…
Generating Robust Adversarial Examples against Online Social Networks (OSNs)
Jun Liu, Jiantao Zhou, Haiwei Wu +2
Online Social Networks (OSNs) have blossomed into prevailing transmission channels for images in the modern era. Adversarial examples (AEs) deliberately designed to mislead deep ne…
Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial Examples
Jun Liu, Jiantao Zhou, Jinyu Tian +1
With the increasing prevalence of cloud computing platforms, ensuring data privacy during the cloud-based image related services such as classification has become crucial. In this…
DifAttack: Query-Efficient Black-Box Attack via Disentangled Feature Space
Liu Jun, Zhou Jiantao, Zeng Jiandian +1
This work investigates efficient score-based black-box adversarial attacks with a high Attack Success Rate (ASR) and good generalizability. We design a novel attack method based on…
Universal Defensive Underpainting Patch: Making Your Text Invisible to Optical Character Recognition
JiaCheng Deng, Li Dong, Jiahao Chen +5
Optical Character Recognition (OCR) enables automatic text extraction from scanned or digitized text images, but it also makes it easy to pirate valuable or sensitive text from the…
Effective Ambiguity Attack Against Passport-based DNN Intellectual Property Protection Schemes through Fully Connected Layer Substitution
Yiming Chen, Jinyu Tian, Xiangyu Chen +1
Since training a deep neural network (DNN) is costly, the well-trained deep models can be regarded as valuable intellectual property (IP) assets. The IP protection associated with…
Exploring Spatial-Temporal Features for Deepfake Detection and Localization
Wu Haiwei, Zhou Jiantao, Zhang Shile +1
With the continuous research on Deepfake forensics, recent studies have attempted to provide the fine-grained localization of forgeries, in addition to the coarse classification at…
Probabilistic Selective Encryption of Convolutional Neural Networks for Hierarchical Services
Jinyu Tian, Jiantao Zhou, Jia Duan
Model protection is vital when deploying Convolutional Neural Networks (CNNs) for commercial services, due to the massive costs of training them. In this work, we propose a selecti…
Self-Supervised Adversarial Example Detection by Disentangled Representation
Zhaoxi Zhang, Leo Yu Zhang, Xufei Zheng +2
Deep learning models are known to be vulnerable to adversarial examples that are elaborately designed for malicious purposes and are imperceptible to the human perceptual system. A…
Detecting Adversarial Examples from Sensitivity Inconsistency of Spatial-Transform Domain
Jinyu Tian, Jiantao Zhou, Yuanman Li +1
Deep neural networks (DNNs) have been shown to be vulnerable against adversarial examples (AEs), which are maliciously designed to cause dramatic model output errors. In this work,…