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<p>The rapid expansion of digital forensics and cloud-based evidence management has introduced new challenges in preserving the confidentiality, integrity, and authenticity of sensitive forensic records. These challenges intensify when evidence is captured using IoT-enabled imaging devices and transmitted across untrusted networks. To address this problem, this paper presents a secure and practical forensic record management framework that integrates a Real-time Crypto Image Camera with a cloud-based Cryptoserver. The proposed system ensures end-to-end protection of forensic images by combining image-dependent chaos-based encryption, SHA-256 hashing, and AES-128 symmetric cryptography, offering strong confidentiality while remaining lightweight enough for resource-constrained edge devices such as the Raspberry Pi 4. A dynamic, image-derived key generation mechanism enhances security against dictionary, rainbow-table, and chosen-plaintext attacks, while an Abstract Data Table (ADT) with hashed credentials ensures protected authentication and controlled access. Experimental evaluation conducted on benchmark and real-time forensic images demonstrates high entropy (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\varvec{\approx } \varvec{7.99}\)</EquationSource> </InlineEquation>), strong diffusion properties (NPCR &gt; 99.6 %), and uniform pixel change rates (UACI <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\varvec{\approx } \varvec{33.4 \%)}\)</EquationSource> </InlineEquation>), confirming the superior randomness and resilience of the proposed scheme compared with recent chaos-based methods. Real-time deployment achieved an average encryption latency of 0.38 s for <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\varvec{512} {\times } \varvec{512}\)</EquationSource> </InlineEquation> images on Raspberry Pi 4, validating its suitability for live forensic operations. Overall, the proposed framework provides an efficient, scalable, and privacy-preserving solution for secure forensic data acquisition, storage, and transmission, enhancing both investigative reliability and operational security.</p>

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Securing forensic records with real-time crypto image camera, authentication, and cloud cryptoserver

  • 期刊论文

  • 发表日期：

    2026年2月18日

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  • C. Sekar,
  • Vinod Ramesh Falmari,
  • T. Janani,
  • Ravindra Yallappa Gangundi,
  • M. Brindha

摘要

The rapid expansion of digital forensics and cloud-based evidence management has introduced new challenges in preserving the confidentiality, integrity, and authenticity of sensitive forensic records. These challenges intensify when evidence is captured using IoT-enabled imaging devices and transmitted across untrusted networks. To address this problem, this paper presents a secure and practical forensic record management framework that integrates a Real-time Crypto Image Camera with a cloud-based Cryptoserver. The proposed system ensures end-to-end protection of forensic images by combining image-dependent chaos-based encryption, SHA-256 hashing, and AES-128 symmetric cryptography, offering strong confidentiality while remaining lightweight enough for resource-constrained edge devices such as the Raspberry Pi 4. A dynamic, image-derived key generation mechanism enhances security against dictionary, rainbow-table, and chosen-plaintext attacks, while an Abstract Data Table (ADT) with hashed credentials ensures protected authentication and controlled access. Experimental evaluation conducted on benchmark and real-time forensic images demonstrates high entropy ( \(\varvec{\approx } \varvec{7.99}\) ), strong diffusion properties (NPCR > 99.6 %), and uniform pixel change rates (UACI \(\varvec{\approx } \varvec{33.4 \%)}\) ), confirming the superior randomness and resilience of the proposed scheme compared with recent chaos-based methods. Real-time deployment achieved an average encryption latency of 0.38 s for \(\varvec{512} {\times } \varvec{512}\) images on Raspberry Pi 4, validating its suitability for live forensic operations. Overall, the proposed framework provides an efficient, scalable, and privacy-preserving solution for secure forensic data acquisition, storage, and transmission, enhancing both investigative reliability and operational security.