CHALLENGES AND PERSPECTIVES OF THE DIGITAL TRACE IN THE ERA OF ARTIFICIAL INTELLIGENCE AND DEEPFAKE TECHNOLOGIES

Abstract

The study investigates the effects of artificial intelligence, specifically deepfake technologies, on the conceptual meaning, usability, and credibility of the digital trace in criminalistics. It provides both normative and useful solutions while highlighting the primary risks related to the preservation and assessment of digital trace as evidence. The paper offers an analytical and normative overview of the problem by fusing criminalistic theory with modern criminalistics and forensic techniques methods, benchmark datasets like FaceForensics++ and DFDC, and European regulatory tools. It adds synthetic, hybrid, and pseudo traces to the typology of digital traces and presents a Digital Authenticity Framework (D-AUTH)[1] for assessing them. In addition to creating new machine-produced artifacts, artificial intelligence also questions accepted ideas of causality and authenticity. Three layers must be integrated for an assessment to be effective: provenance assurance (such as C2PA), criminalistics/forensic content analysis with cross-verification, and procedural integrity through chain-of-custody documentation and cryptographic hashing, all of which are backed by probabilistic and transparent reasoning. Ongoing benchmarking projects like NIST Open MFC are crucial since detection models are still susceptible to compression, out-of-distribution data, and new generative approaches. The article presents a cogent strategy for maintaining the evidential credibility of digital traces for criminal proceedings by tying together criminalistic theory, operational methodology, and the European legal framework (AI Act and Council of Europe Convention).