14 papers
NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results
Wenbin Zou, Tianyi Liu, Kejun Wu +37
This paper reports on the NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration (BSCVR). The challenge aims to advance research on recovering visually coherent videos from…
CAAP: Capture-Aware Adversarial Patch Attacks on Palmprint Recognition Models
Renyang Liu, Jiale Li, Jie Zhang +6
Palmprint recognition is deployed in security-critical applications, including access control and palm-based payment, due to its contactless acquisition and highly discriminative r…
REFORGE: Multi-modal Attacks Reveal Vulnerable Concept Unlearning in Image Generation Models
Yong Zou, Haoran Li, Fanxiao Li +5
Recent progress in image generation models (IGMs) enables high-fidelity content creation but also amplifies risks, including the reproduction of copyrighted content and the generat…
Lyapunov Stable Graph Neural Flow
Haoyu Chu, Xiaotong Chen, Wei Zhou +4
Graph Neural Networks (GNNs) are highly vulnerable to adversarial perturbations in both topology and features, making the learning of robust representations a critical challenge. I…
Proxy-Embedding as an Adversarial Teacher: An Embedding-Guided Bidirectional Attack for Referring Expression Segmentation Models
Xingbai Chen, Tingchao Fu, Renyang Liu +2
Referring Expression Segmentation (RES) enables precise object segmentation in images based on natural language descriptions, offering high flexibility and broad applicability in r…
D-Judge: How Far Are We? Assessing the Discrepancies Between AI-synthesized and Natural Images through Multimodal Guidance
Renyang Liu, Ziyu Lyu, Wei Zhou +1
In the rapidly evolving field of Artificial Intelligence Generated Content (AIGC), a central challenge is distinguishing AI-synthesized images from natural ones. Despite the impres…
STBA: Towards Evaluating the Robustness of DNNs for Query-Limited Black-box Scenario
Renyang Liu, Kwok-Yan Lam, Wei Zhou +4
Many attack techniques have been proposed to explore the vulnerability of DNNs and further help to improve their robustness. Despite the significant progress made recently, existin…
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…