From Capture to Court in Crime Scene Investigations: A Blockchain-Based Tamper-Proof File Sharing System Utilizing IPFS Multihash
Bibliographic record
Abstract
This research paper examines the application of blockchain technology in crime scene investigations, with a particular focus on enhancing the preservation and security of evidence through the cryptographic watermarking of images. Despite several research initiatives, there has been limited investigation into the practical applications of traditional crime scene data sharing. Five LiDAR-enabled devices were selected for their ability to generate accurate 3D point clouds: the iPad M1, iPad M2 Pro, iPhone 14 Pro, iPhone 14, and iPhone 15 Pro Max. The InterPlanetary File System (IPFS) served as the medium for generating Secure Hash Algorithm (SHA)-256 hash values, with two Internet of Things (IoT) workstations evaluated based on tampering and throughput metrics. Throughput testing revealed no correlation between file size and transfer times over IPFS. Notably, the iPhone 14 Pro outperformed the iPad M1 Pro, achieving superior transfer performance despite the iPad's higher file density of 3287.04 KB compared to the iPhone's 2560 KB. Specifically, the iPhone transferred files in 87 seconds, while the iPad Pro took 141 seconds. This suggests that storage size does not significantly impact data transfer efficiency on the network. In the tampering evaluation, files uploaded to IPFS were hashed and distributed via InterPlanetary Name System (IPNS). Workstation Two received and modified these files, which were subsequently re-uploaded. Ultimately, Workstation One successfully detected the alterations. The manipulation techniques employed resulted in slight deviations in hash values, ensuring that the originally uploaded data retains a unique key that triggers alerts if any changes are made.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".