fingerprint_strength_map — IMGFORENSICS `sensor` op

• Data kinds: fingerprint → image2d

• Call: import imgforensics; imgforensics.fingerprint_strength_map(fingerprint, block: 'int' = 16) -> 'np.ndarray' (or opsimgforensics.get("fingerprint_strength_map"))

Usage

A map of the fingerprint's effective strength per block (its standard deviation). `image2d`.

> The detailed description below is the original text — the summary and the headings are translated.

PRNU は飽和した画素と真っ暗な画素では出ない(乗法的な欠陥なので信号が要る)。

この地図は「指紋がどこで何も言えないか」を見るためのもので、値が低い領域の

照合結果は弱い。`block 角の非重複ブロックごとの標準偏差を、元の (H, W)` へ

ブロック定数で戻して返す(端は端のブロックの値で埋める)。

実測(`tests/test_imgforensics.py::test_strength_map_marks_the_saturated_half`、

128x128 の左半分だけを飽和させた 8 枚から指紋を作る):飽和側の平均強度

0.075 に対し通常側 1.409 = 18.8 倍。左半分では PRNU が乗る余地が

無い(乗法的な欠陥なので信号が要る)ことがそのまま出ている。

これは `fingerprint` 語彙の出口でもある(袋小路を作らないため)。

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• image_forensics_audit — py -3.11 examples/image_forensics_audit.py

Ops the type connects to (they accept `image2d` as input)

perceptual_hash · fingerprint_correlate · error_level_map · jpeg_quality_estimate · jpeg_ghost_map · noise_inconsistency_map · copy_move_regions · watermark_embed

Same category (`sensor`)

sensor_fingerprint · fingerprint_correlate

*Provenance: imgforensics.py — IMGFORENSICS operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

fingerprint_strength_map — IMGFORENSICS sensor op