自動車用ドライブレコーダー映像におけるマルチ特徴量フォレンジック分析と1D畳み込みニューラルネットワークによる時間的改ざん検出
Ali Rehman Shinwari1,2, Uswah Binti Khairuddin1, Mohamad Fadzli Bin Haniff1
1Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia.
Sensors (Basel, Switzerland)
|January 28, 2026
まとめ
本研究では、1D-CNNを用いたドライブレコーダー映像の時間的改ざんを検出する効率的な手法を提案する。この手法は、フレームの挿入、削除、重複を正確に特定し、事故調査のための映像真正性を向上させる。
科学分野:
- コンピュータビジョン
- デジタルフォレンジック
- 機械学習
背景:
- 自動車のダッシュボードカメラは事故調査に不可欠ですが、映像の改ざんは証拠の完全性にとって重大なリスクとなります。
- 既存の編集ツールは時間的操作(フレームの挿入、削除、重複)を容易にし、堅牢な検出方法が必要とされています。
結論:
- 提案されたマルチ特徴量1D-CNNは、時間的改ざん検出のための実用的で解釈可能でリソース効率の高いソリューションを提供します。
- この手法は、インテリジェント交通システムにおける信頼性の高い映像フォレンジックをサポートします。
- ドメインシフトに対する感度が観察されたため、ドメイン適応と拡張に関するさらなる研究が必要であることが示唆されました。
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