7 citations · 21 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
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…