DEEPFAKE DETECTION AS FORENSIC EVIDENCE: MINIMUM TECHNICAL STANDARDS AND ADMISSIBILITY TESTS IN COURT
AUTHOR – DHANU* & MR. SUGITH KUMAR**
* STUDENT AT SCHOOL OF EXCELLENCE IN LAW, THE TAMILNADU DR. AMBEDKAR LAW UNIVERSITY, CHENNAI
* PROFESSOR AT SCHOOL OF EXCELLENCE IN LAW, THE TAMILNADU DR. AMBEDKAR LAW UNIVERSITY, CHENNAI
BEST CITATION – DHANU & MR. SUGITH KUMAR, DEEPFAKE DETECTION AS FORENSIC EVIDENCE: MINIMUM TECHNICAL STANDARDS AND ADMISSIBILITY TESTS IN COURT, INDIAN JOURNAL OF LEGAL REVIEW (IJLR), 6 (3) OF 2026, PG. 684-701, APIS – 3920 – 0001 & ISSN – 2583-2344.
ABSTRACT:
The rapid development of artificial intelligence has enabled the creation of highly realistic synthetic media, commonly known as deepfakes. These manipulated audio, video, and image files pose significant challenges to the integrity of digital evidence and the administration of justice. As deepfake technology becomes more sophisticated, courts increasingly face difficulties in determining the authenticity and reliability of digital content presented as evidence. This study examines the role of deepfake detection in digital forensics and proposes minimum technical standards for identifying manipulated media in legal proceedings.
The paper analyses the technological methods used in deepfake detection, including machine learning–based forensic tools, metadata analysis, artifact detection, and biometric inconsistencies. It emphasizes the importance of establishing standardized forensic procedures, including proper chain of custody, validation of detection tools, reproducibility of results, and expert verification. Without such standards, the risk of wrongful admission or rejection of digital evidence may undermine the fairness of trials.
In addition, the study explores the admissibility tests applied by courts when evaluating digital evidence. These include relevance, authenticity, reliability, and compliance with evidentiary rules governing electronic records. The paper discusses how existing legal frameworks for electronic evidence can be adapted to address deepfake-related challenges, highlighting the need for clear judicial guidelines and expert testimony in evaluating AI-generated content.
The possibility of fabricated visual or audio material being presented as genuine evidence raises significant challenges for courts, investigators, and forensic experts. Therefore, establishing reliable deepfake detection methods and defining minimum technical standards for forensic examination have become essential for ensuring the credibility of digital evidence.
This study examines the role of deepfake detection as forensic evidence and explores the minimum technical requirements necessary for identifying manipulated media. It discusses the use of advanced forensic tools, including artificial intelligence–based detection algorithms, metadata analysis, frame-level examination, and biometric inconsistencies, which help experts determine the authenticity of digital content. The research also highlights the importance of maintaining the chain of custody, proper documentation, and verification procedures during the forensic investigation process.