Essentially, the target of deepfake detection problem is to represent natural faces and fake faces at the representation space discriminatively, and it reminds us whether we could optimize the feature extraction procedure at the representation space through constraining intra-class consistence and inter-class inconsistence to bring the intra-class representations close and push the inter-class representations apart?
For instance-level explanation, in order to reveal the relations between high-level semantics and detailed spatial information, this paper proposes a novel cognitive approach to neural networks, which named PANE.
To validate our approach, fMRI data of 143 normal and 100 ADHD affected children is used for experimental purpose.
In this paper, a novel Unified Multi-Task Learning Framework of Real-Time Drone Supervision for Crowd Counting (MFCC) is proposed, which utilizes an image fusion network architecture to fuse images from the visible and thermal infrared image, and a crowd counting network architecture to estimate the density map.
Although the deepfake detection based on convolutional neural network has achieved good results, the detection results show that these detectors show obvious performance degradation when the input images undergo some common transformations (like resizing, blurring), which indicates that the generalization ability of the detector is insufficient.
In this way, rich image appearance models together with more contextual information are integrated by learning a series of decision tree ensembles.