We propose a novel deep convolution neural network called VTD-Net to recognize faces generated by adversarial learning. The network is full-pipeline which composed of face location, interception, scaling and de
we conduct training and testing on FaceForensics++ dataset, and evaluate the generalization capability on Celeb-DF dataset. From the experimental results, we can find that the proposed method has a better or comparable performance, especially in the term of generalization...
Existing deepfake detection methods frequently struggle to achieve reliable results when processing low-quality facial videos, primarily due to artifacts, noise, and other interfering factors. To address this issue, we propose a dual-branch fusion-network model based on physiological features (DPFFNet)...
In order to improve the cross-dataset detection performance of the model, this paper proposes a multi-feature fusion network based on two-stream extraction and multi-scale enhancement. First, we design a two-stream feature extraction module to obtain richer feature information. Secondly, the multi...
To address this, we have constructed a large-scale evaluation benchmark called DeepFaceGen, aimed at quantitatively assessing the effectiveness of face forgery detection and facilitating the iterative development of forgery detection technology. DeepFaceGen consists of 776, 990 real face image/video ...
Fixes: "RAM issue", "No detection" for MaskingHelper 0.5.0BETA2 You can now build a blended face model from a batch of face models you already have, just add the "Make Face Model Batch" node to your workflow and connect several models via "Load Face Model" ...
Implicit Identity Driven Deepfake Face Swapping Detection Baojin Huang†, Zhongyuan Wang†*, Jifan Yang†, Jiaxin Ai†, Qin Zou†, Qian Wang‡, Dengpan Ye‡ †NERCMS, School of Computer Science, Wuhan University ‡School of Cyber Science and Engineering, Wuhan ...
On the trained Inception-ResNet-v1, we used three structurally identical "block8" modules to capture deep semantic features specific to different branches of images. The experiments were also conducted on an NVIDIA A800 80GB GPU using the PyTorch framework. Face detection used the Face Cascade ...
data has significant variability. Ensemble-based methods [43–48] can often enhance generalization by leveraging multiple models to capture different aspects of the data. For example, Bonettini et al. [43] proposed a method for video face manipulation detection based on ensembling multiple CNN ...
Paper tables with annotated results for DeepFake Detection Based on the Discrepancy Between the Face and its Context