The pipeline does not port
the problem
The obvious plan for the Apple version was to reuse the Android core. It does not survive contact: decode, GPU effects and inference are each welded to an Android-only API. Attempting a shared layer would have produced a lowest-common-denominator abstraction that fought both platforms.
what I did
I accepted the rewrite and moved the shared asset up a level: instead of shared code, the Android build plus its QA clips became the parity suite. The Swift version is correct when its output matches the Android output on the same inputs, which is a far stronger contract than shared source would have given.
A detector that fires late lets frames through
the problem
A classifier decides frame by frame, so it necessarily fires on the frame that already contains what you did not want to see. Filtering only the flagged frames means the user sees the thing, briefly, every single time.
what I did
Detection and rendering are separated by an edit decision list. A first pass builds spans with pre-roll before each detection and hysteresis so a flickering detector does not produce flickering censorship; the second pass renders those spans. The cost is two passes over the video, which is worth it.
Feature-length video on a phone
the problem
Stem separation and per-frame inference over a two-hour file is long enough that the OS will kill the app, the user will switch away, or the battery will run out mid-job. Restarting from zero each time makes the feature unusable.
what I did
Jobs are checkpointed per segment and survive an app kill or a reboot. Processing resumes where it stopped rather than restarting. Progress is reported per stage with a live estimate, so a long job at least looks like a long job instead of a frozen screen.
Being honest about what it cannot do
the problem
The failure mode of this category of app is overpromising. No detector is perfect, and a user who believes the filter is absolute is worse off than one who knows its limits.
what I did
The limits are documented in the README and stated in the app: uncertain detections are censored rather than skipped, an occasionally over-blurred frame is the accepted cost, and the output should be verified before it is relied on. The bias is deliberate and it is written down.
