Build the worker as a cascade: use SHA-256 to find files with identical bytes, a configured perceptual hash to generate candidates that may look alike, then FFmpeg’s MPEG-7 video signature to compare the reduced set. Treat the latter two stages as similarity checks, not proof of identity. No universal perceptual-hash settings or validated accuracy figures are established here, so tune and measure the pipeline on your own collection before allowing it to delete anything.
How the three stages divide the work
Each stage answers a different question. SHA-256 fingerprints the complete input data; a perceptual hash represents visual content according to a chosen algorithm and sampling policy; FFmpeg’s signature filter calculates MPEG-7 video signatures and includes lookup functionality. The cascade is an architectural proposal, not a measured performance result. Its ordering aims to remove straightforward exact matches before spending effort on video-level comparisons.
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| Stage | What it tests | Role in the worker | Decision class |
|---|---|---|---|
| SHA-256 | Input bytes | Group likely byte-identical files | Exact-duplicate candidate |
| Perceptual hash | Approximate visual representation | Generate candidates under a documented algorithm, sampling policy, and distance rule | Likely-similar candidate |
| FFmpeg MPEG-7 signature | Video signature similarity and lookup | Compare candidates that make it through earlier stages | Review candidate unless validated for a stricter action |
These are not interchangeable notions of “duplicate.” A transformed copy may have different bytes yet similar imagery; two different videos may show the same subject or scene. Keep identity claims and operational consequences separate.
Stage 1: use SHA-256 for exact-content candidates
Stream each complete file through SHA-256 and store its digest alongside stable asset metadata: path or object key, byte length, ingest time, and the algorithm/version marker. SHA-256 is a message-digest algorithm with a 256-bit output, as specified by the IETF’s RFC 6234. Matching digests are a strong way to group files for exact-duplicate handling, but the digest describes input data—not what a video depicts.
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Do not expect this stage to recognize a transcode, resize, or any other byte-changing operation as the same visual video. Before destructive action, compare the file bytes directly or adopt and document an explicit collision-risk policy. Preserve the original files until that policy and the rest of the worker have been validated.
Stage 2: generate candidates with a perceptual hash
A perceptual-hash stage needs a specific implementation, a rule for selecting frames, and a distance cutoff. None is canonical based on the available evidence, so make each a versioned configuration choice rather than presenting a threshold as a general standard. The purpose is to route plausible visual matches onward, not to certify duplicates.
Choose what the fingerprint sees
Document how frames are sampled and how hashes are compared. Sparse sampling can reduce processing and storage, but may miss brief scenes or changes; denser sampling can capture more of a video while increasing work and the chance that cuts, overlays, or unrelated segments affect comparisons. These are trade-offs to measure, not established benchmark results. Decide whether your expected duplicates include recompression, resizing, letterboxing, crops, frame-rate changes, overlays, or edits, and test each transformation separately.
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Define the distance rule from labeled examples
Build pairs that represent true transformed duplicates and hard negatives—different videos with the same subject, scene, or title. Measure how the chosen distance rule behaves on both. Record the algorithm, implementation version, sampling policy, and cutoff with every stored fingerprint so that comparisons remain interpretable after configuration changes.
Stage 3: compare with FFmpeg’s MPEG-7 video signature
FFmpeg describes its signature video filter as “Calculate the MPEG-7 video signature.” The project’s filter source documentation describes signature calculation and lookup, with lookup modes named off, full, and fast. Use this stage on the reduced candidate set; the source exposes dynamic inputs and a filename option, but exact invocation details should be checked against the documentation for the FFmpeg build you deploy.
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The implementation supports binary and XML output and exposes configurable thresholds for word similarity, composite similarity, frame similarity, minimum matching-sequence length, and the ratio of good frames to all frames. Its documented defaults are th_d=9000, th_dc=60000, th_xh=116, th_di=0, and th_it=0.5. These are FFmpeg implementation defaults, not recommended settings, accuracy guarantees, or universal duplicate-detection cutoffs.
The documented pixel-format list includes gray, several planar YUV formats, NV12, and NV21. Check compatibility in the exact build you run and normalize inputs where needed. FFmpeg’s trunk documentation can change, so verify the installed version’s filter documentation before relying on an option or format.
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Build the worker as a versioned cascade
- Ingest: assign each asset a stable identifier and record its path or object key, byte length, and ingest time.
- Fingerprint bytes: stream the full file through SHA-256. Group digest matches as exact-content candidates, and verify bytes or apply your documented risk policy before any destructive action.
- Generate visual candidates: compute the selected perceptual representation using the recorded sampling policy. Apply the tested distance rule to decide which pairs proceed.
- Calculate or look up video signatures: use FFmpeg’s
signaturefilter for the remaining candidates, with the deployed build’s supported options and a versioned threshold configuration. - Record provenance: store fingerprints separately from media, including the algorithm, implementation or FFmpeg build/version, sampling policy, and thresholds used to create them.
- Report first: send similarity matches to review or reversible quarantine. Do not connect a similarity score directly to irreversible deletion until end-to-end results justify that policy.
Validate accuracy and cost on your own archive
There are no established benchmark figures here for the cascade’s speed, precision, recall, or storage cost. Measure it on your codecs, hardware, and collection rather than inferring performance from the stage names. A useful validation set includes known duplicate pairs, hard negatives, and examples of every transformation expected in the ingestion path.
- Measure false positives and false negatives at each stage and across the full cascade.
- Test recompression, resolution changes, crops, borders, frame-rate changes, overlays, and edits independently.
- Measure runtime and storage for byte streaming, decoded-frame processing, and signature comparison on the intended workload.
- Choose cutoffs according to the cost of missing a duplicate versus incorrectly merging distinct assets.
- Change one versioned setting at a time and retain enough provenance to reproduce prior comparisons.
Start with candidate reports or reversible quarantine. Consider automatic deletion only after representative data demonstrates acceptable end-to-end behavior and the exact-duplicate policy is explicit.
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