Summary of Tex-vit: a Generalizable, Robust, Texture-based Dual-branch Cross-attention Deepfake Detector, by Deepak Dagar et al.
Tex-ViT: A Generalizable, Robust, Texture-based dual-branch cross-attention deepfake detector
by Deepak Dagar, Dinesh Kumar Vishwakarma
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Tex-ViT, a novel architecture that enhances CNN features by combining ResNet with a vision transformer. The model combines traditional ResNet features with a texture module that operates in parallel on sections of ResNet before each down-sampling operation. This allows the model to learn shared distinguishing textural characteristics in manipulated samples, achieving high accuracy in cross-domain scenarios. Experiments were performed on various GAN datasets and showed the model’s ability to generalize well and resist post-processing procedures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new AI model called Tex-ViT that helps computers tell real faces from fake ones. The problem is that current models can’t handle different types of pictures or defend against tricks like blurring or adding noise. Tex-ViT combines two ideas: traditional computer vision and newer transformers. It looks at the texture, or patterns, in a picture to decide if it’s real or not. This model was tested on many different kinds of fake face pictures and performed very well. |
Keywords
» Artificial intelligence » Cnn » Gan » Resnet » Vision transformer » Vit
