- 01
Define the workflow learning job
Treat “ai 換臉” as a search job to investigate, not as proof that a SEELE feature exists. First turn the query into a repeatable sequence with explicit inputs, review points, and a reversible handoff. Write the intended audience, source owner, desired change, protected details, reviewer, and delivery condition before selecting any interface or model. That brief keeps the evaluation specific and makes an unsupported assumption visible early. During review, preserve the evidence table, and ask the channel editor to record camera logic before the rights check.
- 02
Prepare inputs for identity transformation
For this topic, assemble documented consent from every identifiable person, authorized media, a legitimate purpose, and a disclosure plan. Record where each source came from, who may use it, and what must remain unchanged. Use a small representative asset for the first pass, keep the original untouched, and define a fallback route so experimentation cannot silently become the production master. During review, preserve the review copy, and ask the channel editor to record evidence freshness before the evidence refresh.
- Confirm ownership, consent, and allowed reuse In the decision log, preserve the delivery checklist, and ask the identity reviewer to record destination fit before the bounded test.
- Preserve an untouched source and version history In the decision log, preserve the reference set, and ask the claims reviewer to record control availability before the bounded test.
- Name the reviewer and acceptance condition In the decision log, preserve the claim inventory, and ask the creative lead to record camera logic before the bounded test.
- 03
Test observable controls for ai 換臉
A bounded evaluation should inspect identity scope, temporal consistency, expression fidelity, edit reversibility, provenance, and disclosure. Change one meaningful variable at a time and record the date, workspace, account context, input, setting, result, and failure. Topic selection can prioritize the question, but it does not establish availability, quality, speed, licensing, or a supported SEELE workflow. In the decision log, preserve the rights memo, and ask the claims reviewer to record reversal cost before the dated decision.
- 04
Review evidence, safety, and policy boundaries
Do not enable impersonation, non-consensual face or body replacement, deceptive endorsements, or evasion of safeguards. Any identifiable face, body, or voice requires explicit permission, a legitimate purpose, disclosure where required, and a human check against impersonation or deceptive endorsement. For third-party products, competitors, plans, models, and platform rules, attach a verification date and primary source; an absent statement is an evidence gap rather than proof of a limitation. At this stage, preserve the rights memo, and ask the brand reviewer to record failure conditions before the fallback decision.
- 05
Approve a reversible production handoff
Before advancing “ai 換臉”, verify consent, inspect every frame for identity errors, preserve source records, and obtain a named human approval. Document remaining manual work, unresolved evidence, destination requirements, and the person accepting the result. The handoff should preserve sources and test notes, allow correction, and avoid promises about output quality, turnaround, business performance, publishing, or access that the evidence does not support. For the named reviewer, preserve the rights memo, and ask the delivery owner to record destination fit before the evidence refresh.