| --- |
| title: LTF Image Detector |
| emoji: 🔎 |
| colorFrom: blue |
| colorTo: indigo |
| sdk: gradio |
| language: en |
| sdk_version: 6.31.0 |
| python_version: '3.11' |
| app_file: app.py |
| pinned: false |
| short_description: Test LTF on your original image files |
| --- |
| |
| # LTF online demo |
|
|
| Upload an original image file to obtain real / AI-generated scores. The model |
| uses the original RGB image and its full set of 224-pixel tiles; it does not |
| resize the entire input to a fixed resolution before inference. The preview |
| is reduced for display only. Fusion at threshold 0.5 is the default. |
|
|
| This custom PyTorch model uses frozen CLIP ViT-L/14, two-level MIL, cross-shuffle |
| during training, and gated fusion. The published checkpoint is epoch 20 from |
| the historical GenImage run, whose logged macro accuracy is 98.39%. |
| These are historical results, not an accuracy guarantee for uploaded images. |
| Scores are model outputs and are not calibrated authenticity probabilities. |
|
|
| ## Configuration |
|
|
| Space variables (Settings → Variables and secrets): |
|
|
| | Variable | Meaning | Default | |
| | --- | --- | --- | |
| | `LTF_MODEL_REPO` | Model repository containing runtime and weights | Required on Hub | |
| | `LTF_MODEL_REVISION` | Model commit SHA | `main`; deployment pins a commit | |
| | `LTF_MODEL_DIR` | Local model folder, for offline testing | Unset | |
| | `LTF_DEVICE` | `auto`, `cpu`, or `cuda:0` | `auto` | |
| | `LTF_CHUNK_SIZE` | Number of CLIP views per forward chunk | `8` | |
| | `LTF_CPU_THREADS` | PyTorch CPU threads | `2` | |
| | `LTF_MAX_PIXELS` | Largest accepted image, in pixels | `16000000` | |
| | `LTF_MAX_VIEWS` | Largest accepted count of local tiles + global view | `400` | |
|
|
| Oversized images are rejected explicitly; their pixels or tiles are not silently |
| removed. Gradio queues one inference at a time. Original image paths and tokens |
| are not returned in prediction JSON. Uploaded files use Gradio temporary storage |
| with periodic cleanup; they are not added to either repository. |
|
|
| The runtime downloads the model at the configured revision, verifies its asset |
| and source hashes, and then loads it once. No training runs on the Space. |
|
|
| For a private model repository, set an appropriately scoped **read** token as |
| the Space secret `HF_TOKEN`. Do not put tokens in app.py or README.md. |
|
|
| ## Local run |
|
|
| Use Python 3.11 and install `requirements.txt`. From this directory: |
|
|
| ```bash |
| LTF_MODEL_DIR=../model_repo python app.py |
| ``` |
|
|
| The web UI and its `predict` API both call the same original-file inference path. |
|
|
| ## Attention and tile inspection |
|
|
| The Analyze button additionally requests the model's existing `return_viz` |
| outputs during the same forward pass. Temporary observation hooks collect the |
| eight L1 across-tile aggregation heads. No model parameters are changed. |
|
|
| - Exact, numbered local crop boxes, including edge-aligned overlap and small-image padding. |
| - L1 tile-attention overlay: mean or any of the eight aggregation heads. |
| - L2 local-view overlay with the global-view attention weight listed separately. |
| - Four ranked local crop cards; clicking a card selects its patch inspector. |
| - Side-by-side original crop, patch overlay and interactive 16×16 weight matrix. |
| - Large score card and branch comparison, plus per-view attention ranking charts. |
| - Responsive desktop/mobile layout; replacing an image clears previous analysis. |
| - A tile selector for inspecting any local tile without rerunning inference. |
| - Exact view-weight table, embedding fusion gate and read-only model/inference parameters. |
| - Historical training settings from the original saved configuration, explicitly labeled Training only. |
|
|
| Colors use a fixed log2 scale relative to uniform attention (`weight × N`): |
| 0.25×, 0.5×, 1×, 2× and 4×. Neutral gray represents the uniform reference; |
| blue is below and orange-red above that reference. Small differences are not |
| stretched into strong colors. L2 uses the count of all views, including Global, |
| for its reference. Overlap uses mean tile weight. Raw weights are available in |
| the ranking charts, weight table and prediction JSON. A displayed percentage is |
| an attention share, not a calibrated class probability. |
| These are model attention values, not pixel-level forgery probabilities or |
| causal explanations. The eight selectable heads belong to the L1 across-tile |
| aggregator; L1 patch pooling and L2 view pooling use gated MIL attention. |
|
|
| The original five-output `predict` API is preserved. The new `explain` API |
| returns prediction results plus visualization images, tables and attention metadata. |
| Changing a head or inspected tile uses per-session cached data, retained for up |
| to an hour. Changing the decision branch or threshold updates the result from |
| the cached scores. Uploading a new image clears previous results. The unchanged |
| checkpoint is used for both endpoints. |
|
|
| ## Clickable GenImage examples |
|
|
| The gallery includes four real and four generated validation images. Click an |
| image to run the same prediction and attention analysis as an uploaded file. |
| Captions show dataset labels, separately from model predictions. Original files, |
| source paths and checksums are recorded in `examples/manifest.json`; attribution |
| is in `examples/SOURCE.md`. |
|
|
| ## Tiling policy |
|
|
| Full 224×224 blocks are taken without overlap. If pixels remain at the right |
| or bottom, a final block is aligned to that boundary; only boundary blocks |
| may overlap. For width 500, horizontal starts are 0, 224, 276. The global view |
| is separate. This corrects earlier uniformly overlapping tiling; weights are |
| unchanged, but predictions can change. Historical benchmark results have not |
| been recomputed with this preprocessing. |
|
|
