Summary of Ugad: Universal Generative Ai Detector Utilizing Frequency Fingerprints, by Inzamamul Alam et al.
UGAD: Universal Generative AI Detector utilizing Frequency Fingerprints
by Inzamamul Alam, Muhammad Shahid Muneer, Simon S. Woo
First submitted to arxiv on: 12 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 UGAD method detects AI-generated images with higher accuracy than existing state-of-the-art methods. It’s a multi-modal approach that transforms RGB images into YCbCr channels, applies an Integral Radial Operation to emphasize radial features, and then uses Spatial Fourier Extraction for optimal feature extraction. The method finally classifies the data through dense layers using softmax. This approach significantly enhances the accuracy of differentiating between real and AI-generated images, with a 12.64% increase in accuracy and 28.43% increase in AUC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The UGAD method is designed to detect fake images from real ones. It uses special steps to make it hard for computers to fake pictures. First, the computer changes the color of the image to make certain features stand out. Then, it looks at the spatial frequencies (how often things repeat) in the image. Finally, a deep learning network is used to classify the image as real or fake. This method does better than others at finding fake images, with a big increase in how well it works. |
Keywords
» Artificial intelligence » Auc » Deep learning » Feature extraction » Multi modal » Softmax
