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| |
| """ |
| Help Tab |
| Created on Sat Nov 8 11:58:29 2025 |
| @author: standarduser |
| """ |
|
|
| import gradio as gr |
| |
| |
| def create_tab_info(tab_label): |
| """Creates a tab for help text""" |
| with gr.TabItem(tab_label): |
| gr.Markdown(""" |
| # SVAT - Synthetic Video Analyze Tool |
| ## Quick Start |
| 1. **Load Video:** Go to "Video-Frames" tab and upload a video |
| 2. **Navigate Frames:** Use slider or buttons to move through frames |
| 3. **Apply Transformations:** Click transformation buttons to analyze frames |
| 4. **Annotate:** Draw on frames in "Annotations" tab |
| 5. **Analyze Video:** Use "Video Analysis" tab for global analysis |
| ## Frame Transformations |
| ### Laplacian High-Pass |
| Emphasizes high-frequency details and edges. Useful for detecting sharpness artifacts. |
| ### FFT Spectrum |
| Shows frequency domain representation with viridis colormap (blue-green-yellow). |
| Reveals periodic patterns and compression artifacts. |
| ### Error Level Analysis (ELA) |
| Detects JPEG compression artifacts by re-compressing the image. |
| Lower quality = more visible differences in manipulated areas. |
| ### Wavelet Decomposition |
| Multi-scale frequency analysis showing LL, LH, HL, HH subbands. |
| Reveals different frequency components. |
| ### Noise Extraction |
| Isolates high-frequency noise via high-pass filtering. |
| Shows noise patterns that might indicate generation artifacts. |
| ### YCbCr Channels |
| Separates luminance (Y) and chrominance (Cb, Cr) channels. |
| Useful for detecting color space artifacts. |
| ### Gradient Magnitude |
| Visualizes edge strength using Sobel operator. |
| Shows edge consistency. |
| ### Histogram Stretching (CLAHE) |
| Adaptive contrast enhancement that preserves local details. |
| ## Video Analysis |
| ### Mean FFT |
| Calculates average FFT across all frames to detect: |
| - Consistent frequency patterns in AI-generated videos |
| - Generator-specific fingerprints |
| - Temporal artifacts |
| ## Annotation Modes |
| **Per Frame (A):** Separate drawings for each frame |
| **Global (B):** One drawing overlaid on all frames |
| ## Tips for AI Detection |
| - Look for **repeating patterns** in FFT spectrum |
| - Check **ELA** for inconsistent compression levels |
| - Use **Mean FFT** to find generator fingerprints |
| - Compare **noise patterns** between frames |
| - Watch for **unnatural frequency distributions** |
| ## Keyboard Shortcuts |
| *Navigation:* |
| - Use frame slider for quick navigation |
| - Click ◀/▶ buttons for precise frame control |
| ## System Requirements |
| - Python 3.8+ |
| - Gradio 6.x |
| - OpenCV |
| - NumPy 2.x |
| - Pillow |
| - Matplotlib |
| ## About |
| SVAT is designed to help identify synthetic/AI-generated video content through various image analysis techniques. |
| Version: 0.5 |
| Updated: |
| - 29.10.2025 Initial version |
| - 13.01.2026 added "Classify Image" Tab and classify function with XGBoost via image statistics |
| """) |
