Identification of deep network generated images using disparities in color components
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TLDR
This paper proposes a feature set to capture color image statistics for identifying deep network generated (DNG) images and shows that the DNG images are more distinguishable from real ones in the chrominance components, especially in the residual domain.About:
This article is published in Signal Processing.The article was published on 2020-09-01 and is currently open access. It has received 162 citations till now. The article focuses on the topics: Real image & Color image.read more
Citations
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“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告
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Exposing Deep Fakes Using Inconsistent Head Poses
Xin Yang,Yuezun Li,Siwei Lyu +2 more
TL;DR: This paper proposes a new method to expose AI-generated fake face images or videos based on the observations that Deep Fakes are created by splicing synthesized face region into the original image, and in doing so, introducing errors that can be revealed when 3D head poses are estimated from the face images.
Proceedings ArticleDOI
Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations
TL;DR: It is shown that relatively simple visual artifacts can be already quite effective in exposing such manipulations, including Deepfakes and Face2Face.
Proceedings ArticleDOI
Face X-Ray for More General Face Forgery Detection
TL;DR: A novel image representation called face X-ray is proposed, which only assumes the existence of a blending step and does not rely on any knowledge of the artifacts associated with a specific face manipulation technique, and can be trained without fake images generated by any of the state-of-the-art face manipulation methods.
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FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals
TL;DR: It is shown that biological signals hidden in portrait videos can be used as an implicit descriptor of authenticity, because they are neither spatially nor temporally preserved in fake content.
References
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Karen Simonyan,Andrew Zisserman +1 more
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Auto-Encoding Variational Bayes
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Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.