---
license: apache-2.0
library_name: pytorch
tags:
- image-segmentation
- image-forensics
- tamper-detection
- localization
- unet
- resnet34
---

# TIDAL vR.P.19_U

Tampered image detection and localization model built with a 9-channel
multi-quality RGB ELA input and a UNet with a ResNet-34 encoder.

## Included files

- `checkpoints/best_model.pt`
- `checkpoints/vR.P.19_U_unet_resnet34_mqela_rgb.pth`
- `notebooks/vR.P.19_U Image Detection and Localisation.ipynb`

## Notes

- Model version: `vR.P.19_U`
- Primary task: image tampering localization with image-level tamper detection
- Input representation: 9-channel multi-quality RGB ELA at Q=75/85/95

## Minimal load example

```python
import segmentation_models_pytorch as smp
import torch

model = smp.Unet(
    encoder_name="resnet34",
    encoder_weights=None,
    in_channels=9,
    classes=1,
    activation=None,
)

state = torch.load("checkpoints/best_model.pt", map_location="cpu", weights_only=False)
weights = state.get("model_state_dict", state) if isinstance(state, dict) else state
model.load_state_dict(weights, strict=False)
model.eval()
```