deepfakes/faceswap

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Scan timing: clone 3.11s · analysis 22.75s · 6.7 MB · GitHub API rate-limit (preflight)

https://github.com/deepfakes/faceswap · scanned 2026-06-05 10:25 UTC (5 days, 16 hours ago) · 10 languages

367 raw signals (123 security + 244 graph) 75th percentile · Python · medium (20-100K LoC) System graph score 93 (lower by 15)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 5 days, 16 hours ago · v2 · 150 actionable findings from 2 signal sources. 95 repeated signals grouped for readability. Security checks, system graph analysis, and verified AI-agent feedback are merged into one review queue.

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77.8% cov · 103 findings

Score breakdown â 2026-05-18-v5

Component	Sub-score	Weight	Contribution
`structure_score`	85.0	0.15	12.75
`security_score`	100.0	0.25	25.00
`testing_score`	59.0	0.20	11.80
`documentation_score`	87.0	0.15	13.05
`practices_score`	75.0	0.15	11.25
`code_quality`	38.7	0.10	3.87
Overall		1.00	77.7

Severity distribution — click a segment to filter

Active filters: excluding tests × Reset all

Severity: Critical 0 High 7 Medium 6 Low 91 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Security checks 47 System graph 103 Crowd 0 Layer: Quality 82 Cicd 2 Software 57 Api 1 Frontend 1 Security 7

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Bug-class explainers. Each card groups findings of the same shape — these are the patterns most likely to ship to prod and reappear in future scans unless you systematically fix the cause, not just the instance.

Duplicates & near-duplicates 4 findings

What it is: Same function copy-pasted into multiple modules with minor variations.

Why it matters: Each copy drifts independently — bug fixes apply to one, miss the others.

How AI causes it: AI completes the same pattern in each file rather than refactoring to a shared helper.

Fix approach: Extract the duplicated logic into the most general module both call sites already import. Add tests at the helper level.

4 matching findings on this repo

low Near-duplicate function bodies in 2 places repo-level
low Near-duplicate function bodies in 5 places
low Near-duplicate function bodies in 3 places repo-level
low Near-duplicate function bodies in 7 places repo-level

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Legacy markers 9 findings

What it is: TODO, FIXME, XXX, HACK comments. Often indicate a known-broken path the author meant to fix.

Why it matters: Each marker is an unfinished thought. Production code shouldn't ship with debt that's documented but not tracked.

How AI causes it: AI mirrors the style of the codebase, so existing TODOs propagate into new code.

Fix approach: Convert each into a ticket. Delete the comment when the ticket lands. Use a pre-commit hook to block new TODOs without an issue link.

9 matching findings on this repo

low Old/deprecated-named symbol `offsets_legacy` in scripts/extract.py:710
low Old/deprecated-named symbol `is_legacy` in lib/align/aligned_face.py:133
low Old/deprecated-named symbol `is_legacy` in lib/align/detected_face.py:423
low Old/deprecated-named symbol `_update_legacy` in lib/align/alignments.py:552
low Old/deprecated-named symbol `is_legacy` in lib/gui/project.py:549
low Old/deprecated-named symbol `keras_legacy` in lib/model/optimizers/__init__.py:5
low Old/deprecated-named symbol `_cache_offsets_legacy` in lib/infer/objects.py:62
low Old/deprecated-named symbol `_from_legacy` in lib/training/optimizer.py:273
low Old/deprecated-named symbol `_update_legacy` in plugins/train/model/_base/io.py…

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Commented-out code 5 findings

What it is: Lines of source that were intentionally disabled but never deleted.

Why it matters: Git already remembers history — commented code rots, becomes wrong, and adds noise to diffs.

How AI causes it: AI sometimes comments out broken code instead of fixing it. Reviewers approve out of inertia.

Fix approach: Delete. Trust `git log`. If you really need to remember, save it in a notes file under `docs/`.

5 matching findings on this repo

info Commented-code block (10 lines) in lib/align/alignments.py:26
info Commented-code block (5 lines) in lib/gui/analysis/event_reader.py:316
info Commented-code block (10 lines) in lib/model/optimizers/adabelief.py:18
info Commented-code block (6 lines) in plugins/extract/detect/mtcnn.py:148
info Commented-code block (6 lines) in plugins/extract/mask/bisenet_fp.py:161

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Config drift 3 findings

What it is: Settings duplicated across env files, Docker compose, K8s, and code defaults, all with slightly different values.

Why it matters: Production behaviour depends on whichever copy your loader reads first. Subtle bugs in staging that don't reproduce in dev.

How AI causes it: AI writes new config from memory rather than reading the existing source.

Fix approach: Pick one source of truth (env vars + a settings module). Have every other place import from there. Lint for duplicates in CI.

3 matching findings on this repo

high Phantom test coverage: test_FaceswapConfig_save tests/lib/config/config_test.py:221
high Phantom test coverage: test_ConfigFile_save tests/lib/config/ini_test.py:66
high Phantom test coverage: test_ConfigFile_load tests/lib/config/ini_test.py:54

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