音声ディープフェイク検出クイズ

クイズに答えると、答えと解説が表示されます。

1. オーディオディープフェイク検出器の中心的な目標は何ですか?
   • 音声が本物か AI 合成かを判断するには
   • 録音の音質を改善するには
   • 音声をテキストに書き写すには
   • オーディオファイルを圧縮するには
2. In “Does Audio Deepfake Detection Generalize?”, which feature comparison improved performance in the authors’ controlled evaluation?
   • Raw transcript tokens instead of audio
   • CQT or log-spectrogram features instead of mel-spectrogram features
   • A larger waveform file instead of a smaller one
   • Video frames instead of speech features
3. 音声ディープフェイク検出が「軍拡競争」と言われるのはなぜですか?
   • 探知機は軍用ハードウェアで実行されます
   • 音声ファイルは大きくなり続ける
   • 新しい合成手法は、古い偽物で訓練された検出器を回避します
   • 操作には2人が必要です
4. 有名な ASVspoof ベンチマークは何を評価しますか?
   • 音楽の推奨品質
   • テキスト翻訳の精度
   • 画像生成のリアリズム
   • 音声に対するスプーフィング防止/ディープフェイク検出
5. What can a valid C2PA Content Credential help a verifier check?
   • A signed record of an asset’s provenance and edit history
   • That every spoken claim in the audio is true
   • The speaker’s real-world identity in all cases
   • That a recording has never been compressed
6. What input type did the ASVspoof 2021 Deepfake task target?
   • Uncompressed music recordings from a studio
   • Manipulated, compressed speech posted online
   • Images altered by face-swapping systems
   • Only live telephone conversations with known speakers
7. How large was the real-world dataset collected in the cited audio deepfake generalization study?
   • 3.79 hours total, all authentic speech
   • 37.9 hours of deepfakes with no authentic audio
   • 37.9 hours total, including 17.2 hours of deepfakes
   • 172 hours total, including 17.2 hours of deepfakes
8. What does the absence of a C2PA credential establish?
   • That the recording is necessarily a deepfake
   • That the recording has no edits
   • That the speaker is verified
   • Nothing by itself about whether the media is authentic or synthetic