Summary of Detecting Facial Image Manipulations with Multi-layer Cnn Models, by Alejandro Marco Montejano and Angela Sanchez Perez and Javier Barrachina and David Ortiz-perez and Manuel Benavent-lledo and Jose Garcia-rodriguez
Detecting Facial Image Manipulations with Multi-Layer CNN Models
by Alejandro Marco Montejano, Angela Sanchez Perez, Javier Barrachina, David Ortiz-Perez, Manuel Benavent-Lledo, Jose Garcia-Rodriguez
First submitted to arxiv on: 9 Dec 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 This research develops convolutional neural networks (CNNs) tailored for detecting manipulated digital images, particularly those generated using stable diffusion and mid-journey techniques. The study compares three progressively complex CNN architectures, assessing their ability to classify and localize manipulations across various facial image modifications. Regularization and optimization techniques are used to improve feature extraction and performance. The results show that the proposed models achieve an accuracy of up to 76% in distinguishing manipulated images from genuine ones, outperforming traditional approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates special computers (CNNs) that can spot fake digital pictures. These fake pictures look very real and are made using new techniques like stable diffusion and mid-journey. The researchers compare different types of CNNs to see which one is best at finding the fake images. They also try to make the computers better by adding extra features and training them in a special way. In the end, they show that their approach can accurately spot fake pictures up to 76% of the time, which is better than other methods. |
Keywords
» Artificial intelligence » Cnn » Diffusion » Feature extraction » Optimization » Regularization
