Blockchain-Enhanced Chain of Custody with Deep Learning Tampering Detection for Digital Forensics

Abstract

The preservation of digital evidence integrity currently faces critical challenges, notably manual chain-of-custody errors ranging from 15-20%, the proliferation of sophisticated tampering techniques, and inherent scalability limitations. To address these issues, this paper presents an integrated framework that synergistically combines blockchain-based immutable custody chains, dual-branch convolutional neural networks for tampering detection, and hybrid consensus mechanisms. Through systematic ablation studies, we demonstrate that the proposed smart contract automation effectively mitigates manual custody errors, ensuring a tamper-evident and immutable custody record. Furthermore, our dual-branch architecture, enhanced with adaptive fusion (α =0.6), attains a 98.5% tampering detection accuracy—representing a 5.3% improvement over single-branch baselines. Additionally, the hybrid Proof-of-Authority and Byzantine Fault Tolerant consensus mechanism delivers a throughput of 10,000 transactions per second, marking a 1,429-fold improvement over traditional blockchain implementations. A comprehensive evaluation on the NIST CFReDS, supported by statistical validation (p < 0.001), demonstrates the superiority of our approach over six baseline methods. We further provide a detailed failure analysis, a computational cost breakdown, and validation through simulated forensic scenarios, alongside proposed integration pathways for commercial forensic tools such as EnCase and FTK to facilitate practical adoption.