7 papers · 1 filter
SSTA: Salient Spatially Transformed Attack
Renyang Liu, Wei Zhou, Sixin Wu +2
Extensive studies have demonstrated that deep neural networks (DNNs) are vulnerable to adversarial attacks, which brings a huge security risk to the further application of DNNs, es…
Double-Flow-based Steganography without Embedding for Image-to-Image Hiding
Bingbing Song, Derui Wang, Tianwei Zhang +3
As an emerging concept, steganography without embedding (SWE) hides a secret message without directly embedding it into a cover. Thus, SWE has the unique advantage of being immune…
Can LSH (Locality-Sensitive Hashing) Be Replaced by Neural Network?
Renyang Liu, Jun Zhao, Xing Chu +3
With the rapid development of GPU (Graphics Processing Unit) technologies and neural networks, we can explore more appropriate data structures and algorithms. Recent progress shows…
Model Inversion Attacks on Homogeneous and Heterogeneous Graph Neural Networks
Renyang Liu, Wei Zhou, Jinhong Zhang +3
Recently, Graph Neural Networks (GNNs), including Homogeneous Graph Neural Networks (HomoGNNs) and Heterogeneous Graph Neural Networks (HeteGNNs), have made remarkable progress in…
AFLOW: Developing Adversarial Examples under Extremely Noise-limited Settings
Renyang Liu, Jinhong Zhang, Haoran Li +3
Extensive studies have demonstrated that deep neural networks (DNNs) are vulnerable to adversarial attacks. Despite the significant progress in the attack success rate that has bee…
SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack
Renyang Liu, Jinhong Zhang, Kwok-Yan Lam +2
Previous studies have revealed that artificial intelligence (AI) systems are vulnerable to adversarial attacks. Among them, model extraction attacks fool the target model by genera…
Boosting Black-box Attack to Deep Neural Networks with Conditional Diffusion Models
Renyang Liu, Wei Zhou, Tianwei Zhang +3
Existing black-box attacks have demonstrated promising potential in creating adversarial examples (AE) to deceive deep learning models. Most of these attacks need to handle a vast…