Summary of Detecting Inpainted Video with Frequency Domain Insights, by Quanhui Tang and Jingtao Cao
Detecting Inpainted Video with Frequency Domain Insights
by Quanhui Tang, Jingtao Cao
First submitted to arxiv on: 21 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed Frequency Domain Insights Network (FDIN) detects manipulated regions in inpainted videos by incorporating insights from the frequency domain. This network features an Adaptive Band Selective Response module to discern frequency characteristics specific to various inpainting techniques and a Fast Fourier Convolution-based Attention module for identifying periodic artifacts in inpainted regions. The FDIN also utilizes 3D ResBlocks for spatiotemporal analysis, progressively refining detection precision from broad assessments to detailed localization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to detect manipulated regions in videos that have been edited using the video inpainting technique. This is important because editing videos without permission can be illegal and unethical. The authors suggest that previous methods for detecting these edits were not very good, so they developed a new approach called Frequency Domain Insights Network (FDIN). The FDIN works by looking at the frequency of different parts of the edited video to see if it looks like something was added or changed. This helps the model detect where changes have been made in the video. |
Keywords
» Artificial intelligence » Attention » Precision » Spatiotemporal
