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By keeping benchmark-native outcomes distinct from pooled summaries, the study makes test-population, class-balance, and missing-record-coverage differences visible and supports interpretation of detector results in research, platform-safety, and forensic-review settings, foregrounding traceable protocol conditions over claims or leaderboard comparisons.
Current DeepFake detectors can be fooled by semantically inconsistent real audio and video, highlighting a critical blind spot in their ability to assess realistic manipulations.