5 papers · 2 filters
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…
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…