AI-Based Detection of Deepfake Evidence in Legal Proceedings

Abstract

Rapid advances in generative artificial intelligence have complicated the evidentiary status of digital photographs, video recordings, and audio files by making highly realistic synthetic and manipulated media increasingly accessible. In legal proceedings, the problem extends beyond identifying fabricated content. Courts must determine whether an AI-based detector itself is sufficiently reliable, transparent, reproducible, and explainable to support authentication or expert forensic conclusions. This study develops a simulation-based framework for evaluating AI-assisted deepfake detection within legal-evidence workflows. A synthetic corpus of 1,600 digital-evidence observations was modeled across native, social-media-compressed, low-light or noisy, and re-encoded or edited conditions. Four analytical configurations were evaluated: a conventional convolutional neural network forensic detector, a vision-transformer detector, a multimodal detection system, and a governed multimodal architecture supplemented by provenance verification, confidence calibration, abstention thresholds, audit logging, and expert review. Detection performance was evaluated using balanced accuracy, sensitivity, specificity, F1 score, area under the receiver operating characteristic curve, and false-positive rate. The simulation indicates that technical performance deteriorates when media undergo compression, noise, re-encoding, and post-processing, while multimodal analysis and provenance-informed review provide greater robustness than single-model classification. The governed architecture achieved a simulated overall balanced accuracy of 92.1%, compared with 82.2% for the conventional detector. The study argues, however, that accuracy alone is insufficient for courtroom use. AI-generated  forensic conclusions should be accompanied by preservation of the original evidence, cryptographic integrity verification, documented chain of custody, validated model performance, disclosure of error rates, analysis of demographic and domain bias, explainable forensic indicators, and independent human review. Within the U.S. evidentiary context, these requirements intersect with authentication principles under Federal Rule of Evidence 901 and reliability requirements for expert testimony under Rule 702. The paper concludes that AI deepfake detectors should function as forensic decision-support instruments rather than autonomous arbiters of evidentiary truth.