no code implementations • 22 Nov 2023 • Yaqi Liu, Chao Xia, Song Xiao, Qingxiao Guan, Wenqian Dong, Yifan Zhang, Nenghai Yu
In this paper, we propose a Transformer-style copy-move forgery detection network named as CMFDFormer, and provide a novel PCSD (Pooled Cube and Strip Distillation) continual learning framework to help CMFDFormer handle new tasks.
no code implementations • 5 Jul 2017 • Yaqi Liu, Qingxiao Guan, Xianfeng Zhao
Numerous experiments are conducted to demonstrate the effectiveness and robustness of the GPU version of Convolutional Kernel Network, and the state-of-the-art performance of the proposed copy-move forgery detection method based on Convolutional Kernel Network.
1 code implementation • 13 Jun 2017 • Yaqi Liu, Qingxiao Guan, Xianfeng Zhao, Yun Cao
In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and the segmentation-based multi-scale analysis to locate tampered areas in digital images.