スタートアップアーキテクチャによる圧縮と刺激を用いたビッグデータセキュリティのためのディープラーニングステガングラフィー
Bini M Issac1, S N Kumar2, Sherin Zafar3
1Dept. of Computer Science & Engineering, Amal Jyothi College of Engineering, APJ Abdul Kalam Technological University, Thiruvananthapuram, Kerala, 695 016, India.
Scientific reports
|August 25, 2025
まとめ
この研究は,安全な医療画像ステガノグラフィのための新しいディープラーニングフレームワークを導入し,テレメディシンアプリケーションのデータ完全性とリアルタイムパフォーマンスを保証します. この方法は 視覚的な歪みを最小限に抑えることで 敏感な医療データを効果的に埋め込み 再構築します
科学分野:
- コンピュータ科学
- 医療用イメージング
- サイバーセキュリティ
背景:
- 機密医療データの安全な伝送は 遠隔医療とデジタルフォレンシックのビッグデータによる成長が不可欠です
- 従来のステガノグラフィーは 診断の整合性とノイズと変異に対する強度に問題があります
研究 の 目的:
- 安全な医療画像伝送のための新しい深層学習ベースのステガノグラフィックフレームワークを開発する.
- 診断の完全性と信頼性を保つために,従来の方法の限界に対処する.
主な方法:
- Squeeze-Excitation (SE) ブロック,Inception モジュール,および残留接続を組み合わせたフレームワークを提案しました.
- 秘密の医療画像をカバー画像に埋め込むためにエンコーダーが拡張されたコンボリュションとSE注意を使用します.
- 解読器は,再構築のために残留および多スケールインセプションベースの特徴抽出を使用します.
主要な成果:
- このモデルは,MRIとOCTのデータセットで高いピーク信号比 (PSNR) 値 (39.02,38.75) と構造類似度指数 (SSIM) 値 (0.9757) を達成しています.
- 視界の歪みが最小で ステガノグラフィの効果が確認された
- NVIDIA Jetson TX2でリアルタイムで低電力で展開できるように設計されています.
結論:
- 開発されたディープラーニング・フレームワークは,プライバシーに敏感な環境のための安全で高容量ステガノグラフィック・ソリューションを提供します.
- このモデルのリアルタイムの性能と頑丈さは,遠隔医療とデジタルフォレンシクスの実用的なアプリケーションに適しています.
- この研究は 医療データの安全な処理を進めて 機密性と診断の質の両方を保ちます
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