88 citations · 102 across the 10 of their papers we have counts for
10 papers
Reducing Adversarial Training Cost with Gradient Approximation
Huihui Gong
Deep learning models have achieved state-of-the-art performances in various domains, while they are vulnerable to the inputs with well-crafted but small perturbations, which are na…
Parameter-Saving Adversarial Training: Reinforcing Multi-Perturbation Robustness via Hypernetworks
Huihui Gong, Minjing Dong, Siqi Ma +3
Adversarial training serves as one of the most popular and effective methods to defend against adversarial perturbations. However, most defense mechanisms only consider a single ty…
Stealthy Physical Masked Face Recognition Attack via Adversarial Style Optimization
Huihui Gong, Minjing Dong, Siqi Ma +3
Deep neural networks (DNNs) have achieved state-of-the-art performance on face recognition (FR) tasks in the last decade. In real scenarios, the deployment of DNNs requires taking…
Alpha-Clustering in Nuclei and Its Impact on Nuclear Symmetry Energy
Shuo Yang, Ruijia Li, Chang Xu
Nuclear symmetry energy is a fundamental quantity currently under intense investigation in both nuclear physics and astrophysics. The {\it softness} or {\it stiffness} of symmetry…
GPT Self-Supervision for a Better Data Annotator
Xiaohuan Pei, Yanxi Li, Chang Xu
The task of annotating data into concise summaries poses a significant challenge across various domains, frequently requiring the allocation of significant time and specialized kno…
Two-in-one Knowledge Distillation for Efficient Facial Forgery Detection
Chuyang Zhou, Jiajun Huang, Daochang Liu +4
Facial forgery detection is a crucial but extremely challenging topic, with the fast development of forgery techniques making the synthetic artefact highly indistinguishable. Prior…
Anti-Compression Contrastive Facial Forgery Detection
Jiajun Huang, Xinqi Zhu, Chengbin Du +3
Forgery facial images and videos have increased the concern of digital security. It leads to the significant development of detecting forgery data recently. However, the data, espe…
Recognising Agreement and Disagreement between Stances with Reason Comparing Networks
Chang Xu, Cecile Paris, Surya Nepal +1
We identify agreement and disagreement between utterances that express stances towards a topic of discussion. Existing methods focus mainly on conversational settings, where dialog…
STRIP: A Defence Against Trojan Attacks on Deep Neural Networks
Yansong Gao, Chang Xu, Derui Wang +3
A recent trojan attack on deep neural network (DNN) models is one insidious variant of data poisoning attacks. Trojan attacks exploit an effective backdoor created in a DNN model b…
Cross-Target Stance Classification with Self-Attention Networks
Chang Xu, Cecile Paris, Surya Nepal +1
In stance classification, the target on which the stance is made defines the boundary of the task, and a classifier is usually trained for prediction on the same target. In this wo…