Implementation of BLAKE3 hashing for accelerating digital evidence integrity verification in forensic investigations
DOI:
https://doi.org/10.35335/cit.Vol18.2026.1629.pp151-160Keywords:
Blake3, Chain of Custody, Digital Forensics, Hash Function, MultithreadingAbstract
The evolution of cybersecurity threats demands rapid and legally accountable investigation responses. A crucial principle in digital forensics is maintaining data integrity to ensure the validity of the chain of custody in court using cryptographic hash functions. However, the increasing volume of storage media presents significant technical challenges. Conventional algorithms like SHA-256 process data sequentially, causing hash verification on massive forensic images to take hours. This study aims to evaluate the BLAKE3 algorithm as an accelerator in the digital evidence integrity verification process. The evaluation was conducted using a comparative experimental method between MD5, SHA-256, and BLAKE3 by varying processor core allocations and simulated file sizes up to 50 GB. The test results demonstrated that parallel processing in BLAKE3 significantly reduces execution time. In the 50 GB file test utilizing 8 threads, BLAKE3 achieved a throughput of 5000 MB/s and completed verification in just 10.0 seconds, vastly outperforming SHA-256 which required 142.8 seconds. The application of BLAKE3 proved to provide security equivalent to SHA-256 while accelerating the verification process, thereby supporting more efficient courtroom proceedings without violating legal integrity standards.
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References
A. A. Khan, M. Uddin, A. A. Shaikh, A. A. Laghari, and A. E. Rajput, “MF-Ledger: Blockchain Hyperledger Sawtooth-Enabled Novel and Secure Multimedia Chain of Custody Forensic Investigation Architecture,” IEEE Access, vol. 9, pp. 103637–103650, 2021, doi: 10.1109/ACCESS.2021.3099037.
H. M. Elgohary, S. M. Darwish, and S. M. Elkaffas, “Improving Uncertainty in Chain of Custody for Image Forensics Investigation Applications,” IEEE Access, vol. 10, pp. 14669–14679, 2022, doi: 10.1109/ACCESS.2022.3147809.
R. H. Preston, “Applying Grover’s Algorithm to Hash Functions: A Software Perspective,” 2022, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/TQE.2022.3233526.
K. Jang, H. Seo, and A. Chattopadhyay, “Improved Quantum Cryptanalysis of MD5 and SHA-,” IEEE Access, 2026, doi: 10.1109/ACCESS.2026.3680947.
H. Safdar Gill et al., “A Normalized Exponential Piecewise Chaotic System (NEPCS) and DNA Image Cryptography Using SHA-256,” IEEE Access, vol. 13, pp. 110392–110417, 2025, doi: 10.1109/ACCESS.2025.3582318.
K. Hammar, T. Li, R. Stadler, and Q. Zhu, “Adaptive Security Response Strategies Through Conjectural Online Learning,” IEEE Transactions on Information Forensics and Security, vol. 20, pp. 4055–4070, 2025, doi: 10.1109/TIFS.2025.3558600.
N. Sugiura and D. Fujiki, “SharK: Enabling High-Performance Range Queries in Key-Value Store Through Vlog Resharding,” IEEE Access, vol. 13, pp. 203041–203056, 2025, doi: 10.1109/ACCESS.2025.3638766.
Z. Abdullah Jasim and A. Kadhim Hadi, “Optimizing Blockchain Network Performance Using Blake3 Hash Function in POS Consensus Algorithm,” IEEE Access, vol. 13, pp. 44760–44774, 2025, doi: 10.1109/ACCESS.2025.3546723.
T. H. Tran, H. L. Pham, and Y. Nakashima, “A High-Performance Multimem SHA-256 Accelerator for Society 5.0,” IEEE Access, vol. 9, pp. 39182–39192, 2021, doi: 10.1109/ACCESS.2021.3063485.
H. Choi and S. C. Seo, “Fast Implementation of SHA-3 in GPU Environment,” IEEE Access, vol. 9, pp. 144574–144586, 2021, doi: 10.1109/ACCESS.2021.3122466.
H. L. Pham, T. H. Tran, T. D. Phan, V. T. Duong Le, D. K. Lam, and Y. Nakashima, “Double SHA-256 Hardware Architecture with Compact Message Expander for Bitcoin Mining,” IEEE Access, vol. 8, pp. 139634–139646, 2020, doi: 10.1109/ACCESS.2020.3012581.
Z. Yang, A. Zhang, and Z. Mo, “PsmArena: Partitioned Shared Memory for NUMA-Awareness in Multithreaded Scientific Applications,” 1007.
H. Mohamed, N. Koroniotis, N. Moustafa, F. Schiliro, and A. Y. Zomaya, “Harnessing Federated Learning for Digital Forensics in IoT: A Survey and Introduction to the IoT-LF Framework,” IEEE Open Journal of the Communications Society, vol. 6, pp. 3161–3191, 2025, doi: 10.1109/OJCOMS.2024.3492919.
M. Pourvahab and G. Ekbatanifard, “An efficient forensics architecture in software-defined networking-IoT using blockchain technology,” IEEE Access, vol. 7, pp. 99573–99588, 2019, doi: 10.1109/ACCESS.2019.2930345.
A. Mehmood, A. Shafique, M. Alawida, and A. N. Khan, “Advances and Vulnerabilities in Modern Cryptographic Techniques: A Comprehensive Survey on Cybersecurity in the Domain of Machine/Deep Learning and Quantum Techniques,” IEEE Access, vol. 12, pp. 27530–27555, 2024, doi: 10.1109/ACCESS.2024.3367232.
A. Degada and H. Thapliyal, “Single-Rail Adiabatic Logic for Energy-Efficient and CPA-Resistant Cryptographic Circuit in Low-Frequency Medical Devices,” IEEE Open Journal of Nanotechnology, vol. 3, pp. 1–14, 2022, doi: 10.1109/OJNANO.2021.3135364.
M. Kaveh and A. Falahati, “An improved Merkle hash tree based secure scheme for bionic underwater acoustic communication,” Frontiers of Information Technology and Electronic Engineering, vol. 22, no. 7, pp. 1010–1019, Jul. 2021, doi: 10.1631/FITEE.2000043.
A. Mohammed Ali and A. Kadhim Farhan, “A novel improvement with an effective expansion to enhance the MD5 hash function for verification of a secure E-Document,” IEEE Access, vol. 8, pp. 80290–80304, 2020, doi: 10.1109/ACCESS.2020.2989050.
L. Loffi, G. L. Camillo, C. A. De Souza, C. M. Westphall, and C. B. Westphall, “Management of the Chain of Custody of Digital Evidence Using Blockchain and Self-Sovereign Identities: A Systematic Literature Review,” 2025, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/ACCESS.2025.3560191.
H. H. Kao, “Accelerating Multilingual Cryptocurrency Forensics: An NLP-Driven Approach for Efficient Mnemonic Identification,” IEEE Access, vol. 13, pp. 10513–10526, 2025, doi: 10.1109/ACCESS.2025.3528829.
W. A. Prabowo, F. Mohsen, and S. R. Selamat, “WhatsApp Mobile Applications in the Lens of Digital Forensics: Deciphering the Msgstore.db.crypt14 File,” Journal of Cyber Security and Mobility, vol. 14, no. 4, pp. 823–848, Oct. 2025, doi: 10.13052/jcsm2245-1439.1443.
J. Oh, S. Lee, and H. Hwang, “Forensic Detection of Timestamp Manipulation for Digital Forensic Investigation,” IEEE Access, vol. 12, pp. 72544–72565, 2024, doi: 10.1109/ACCESS.2024.3395644.
J. H. M. Korndorfer, A. Eleliemy, A. Mohammed, and F. M. Ciorba, “LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications,” IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 4, pp. 830–841, Apr. 2022, doi: 10.1109/TPDS.2021.3107775.
T. Mary and C. Sreeja, “Adversarial Shadows in Digital Forensics: New Insights into File Fragment Classification Vulnerabilities and Defenses,” IEEE Access, pp. 1–1, Jan. 2026, doi: 10.1109/access.2026.3655822.
H. Teper, D. Kuhse, M. Gunzel, G. von der Bruggen, F. Howar, and J. J. Chen, “Thread Carefully: Preventing Starvation in the ROS 2 Multithreaded Executor,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 43, no. 11, pp. 3588–3599, 2024, doi: 10.1109/TCAD.2024.3446865.
M. Kaveh and A. Falahati, “An improved Merkle hash tree based secure scheme for bionic underwater acoustic communication,” Frontiers of Information Technology and Electronic Engineering, vol. 22, no. 7, pp. 1010–1019, Jul. 2021, doi: 10.1631/FITEE.2000043.
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Copyright (c) 2026 Mirza Gofur Saleh, H.A. Danang Rimbawa
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
