How can we train deepfake models to generate more realistic and convincing audio, video, and image content?
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Deepfake models can be trained to generate more realistic and convincing audio, video, and image content through various approaches. One approach is to exploit mouth-related mismatches between the auditory and visual modalities in fake videos to enhance generalization to unseen forgeries . Another approach involves capturing the correlation between non-critical phonemes and visemes, and designing a loss function to measure the evolutionary consistency of non-critical phoneme-viseme . Additionally, the use of deep learning architectures such as Convolutional Neural Networks (CNNs), Vision Transformers, and Swin Transformers can improve the generalization capabilities of deepfake models . These architectures excel in different scenarios, with CNNs being effective for datasets with limited elements, Vision Transformers performing well with varied datasets, and Swin Transformers providing good performance in cross-dataset scenarios . By combining these approaches and leveraging self-supervised pre-training strategies, deepfake models can generate more realistic and convincing content .
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| Papers (5) | Insight |
|---|---|
Open access•Posted Content | The paper proposes a novel Audio-Video Deepfake dataset (FakeAVCeleb) that contains deepfake videos and respective synthesized lip-synced fake audios, which can be used to train deepfake models for generating more realistic and convincing audio, video, and image content. |
| The paper does not provide information on training deepfake models to generate more realistic and convincing content. | |
09 Mar 2023 | The paper does not provide information on training deepfake models to generate more realistic and convincing audio, video, and image content. |
12 Jun 2023 | The paper proposes a novel Deepfake detection method called NPVForensics that mines the correlation between non-critical phonemes and visemes to improve detection accuracy. |
12 Jun 2023 | The paper proposes a two-phase audio-driven multi-modal transformer-based framework called AVForensics for deepfake video detection using audio-visual matching and global facial movement features. |
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