risa-recaptured-image-detection

RISA Recaptured-Image Detection

This repository contains the public deployment weights for a binary classifier that distinguishes single-captured images from images recaptured through a display-camera chain.

The release is a deterministic 17-readout RISA model fitted on all 86 development images from 12 content groups in the Imperial College London DS-05 dataset. It uses a frozen 384-dimensional DeiT-S representation together with 479-dimensional edge-profile and 2,035-dimensional native-texture descriptors. The readouts use a linear kernel and aggregate their two-class logits by coordinate-wise median.

Files

• model.safetensors: non-executable model tensors, including full-data standardization statistics, standardized support features, 17 dual coefficient matrices, and readout multipliers.
• config.json: tensor shapes, class mapping, selected deployment configuration, evaluation boundary, and SHA-256 provenance.
• LICENSE.md: release terms for the weights.

This is a weights-only release. The raw-image feature-extraction implementation and training code are not included. The model therefore expects feature vectors produced by a compatible RISA feature pipeline; it is not a drop-in Transformers checkpoint.

Evaluation

The defensible performance estimate comes from a frozen four-outer/three-inner StratifiedGroupKFold evaluation in which each content identifier stays entirely within one fold.

The confusion matrix is [[30, 3], [2, 51]], with true classes as rows and predicted classes as columns.

The downloadable weights are a later deployment refit on all 86 development images. The 97.67% score stored in config.json is the grouped configuration-selection score used to choose that full-data deployment configuration. It is not an independent test estimate and should not replace the 94.19% grouped out-of-fold accuracy above.

Intended use

The model is intended for research and technical evaluation of recaptured-image screening. A positive score indicates that an image resembles the recaptured class represented in DS-05; it is not proof of deception, manipulation, or malicious intent.

Limitations

• The training set contains only 86 images and 12 scene-content groups.
• Content grouping controls scene leakage but does not establish cross-device, cross-display, or cross-session generalization.
• The model has not been validated as a standalone forensic decision system.
• Benign display photography, accessibility workflows, and document capture can create the same acquisition route.
• Human review and original evidence should be retained for consequential decisions.

Dataset provenance

The source dataset is the Imperial College London DS-05 recaptured-image dataset. No original dataset images are redistributed in this repository.

Integrity

model.safetensors SHA-256:

CE78A552A7CBF9A6E5F1FF540F3AB0AD4E86CD03A792643C76EEA49753D2C6C5

The serialization round-trip was verified with a maximum absolute logit error of 0.0.