8 citations · 26 across the 11 of their papers we have counts for
11 papers
Data-Independent Operator: A Training-Free Artifact Representation Extractor for Generalizable Deepfake Detection
Chuangchuang Tan, Ping Liu, RenShuai Tao +4
Recently, the proliferation of increasingly realistic synthetic images generated by various generative adversarial networks has increased the risk of misuse. Consequently, there is…
Enhanced Few-Shot Class-Incremental Learning via Ensemble Models
Mingli Zhu, Zihao Zhu, Sihong Chen +2
Few-shot class-incremental learning (FSCIL) aims to continually fit new classes with limited training data, while maintaining the performance of previously learned classes. The mai…
ToonTalker: Cross-Domain Face Reenactment
Yuan Gong, Yong Zhang, Xiaodong Cun +5
We target cross-domain face reenactment in this paper, i.e., driving a cartoon image with the video of a real person and vice versa. Recently, many works have focused on one-shot t…
Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples
Shaokui Wei, Mingda Zhang, Hongyuan Zha +1
Backdoor attacks are serious security threats to machine learning models where an adversary can inject poisoned samples into the training set, causing a backdoored model which pred…
Inter-frame Accelerate Attack against Video Interpolation Models
Junpei Liao, Zhikai Chen, Liang Yi +3
Deep learning based video frame interpolation (VIF) method, aiming to synthesis the intermediate frames to enhance video quality, have been highly developed in the past few years.…
Improving Fast Adversarial Training with Prior-Guided Knowledge
Xiaojun Jia, Yong Zhang, Xingxing Wei +4
Fast adversarial training (FAT) is an efficient method to improve robustness. However, the original FAT suffers from catastrophic overfitting, which dramatically and suddenly reduc…