“Digital Evidence Integrity Verification Using AI + Blockchain”

Abstract

The credibility of digital evidence is a cornerstone of modern cybercrime investigations, digital forensics, and judicial processes. However, adversarial tampering, deepfake manipulation, and insider threats have raised significant concerns regarding the authenticity and admissibility of such evidence. Conventional integrity-preservation methods—such as hashing, encryption, and secure storage—struggle to meet the demands of scalability, transparency, and resilience in today’s forensic environments. Recent advances in artificial intelligence (AI) and blockchain offer promising avenues for overcoming these limitations. AI techniques contribute to content-level verification by detecting anomalies, forgeries, and manipulations in digital artefacts, while blockchain ensures tamper-proof chain-of-custody management through decentralization, immutability, and auditability. This review synthesizes the state of the art in digital evidence integrity verification through the combined application of AI and blockchain. We examine existing frameworks, datasets, algorithms, and deployment models, while critically analyzing their strengths and limitations. Furthermore, we identify gaps in scalability, explainability, and legal admissibility, proposing future directions such as federated learning, explainable AI, zero-knowledge proofs, and quantum-resistant blockchains. By consolidating research across computer science, law, and digital forensics, this review highlights the potential of AI–blockchain synergy to establish robust, scalable, and trustworthy evidence verification frameworks for real-world forensic and judicial systems.