Empirical Assessment of Deepfake Detection: Advancing Judicial Evidence Verification Through Artificial Intelligence

Deepfake technology poses a profound challenge to the integrity of facial evidence in criminal justice, threatening the authenticity and admissibility of such evidence in the courtroom. In this research, a specialized deepfake detection system tailored for facial evidence verification was developed, aiming to counteract the influence of deepfake technology. The proposed system integrates a unique combination of video-frame selection, confidence thresholds, prediction timestamps, and heat maps for individual frames of suspect videos. This methodological fusion is designed to support forensic analysts by enhancing the reliability and trustworthiness of video evidence used in judicial settings. Our comprehensive evaluation involved diverse user groups participating in experimental scenarios to assess the effectiveness of the system. The results indicated that the combined features of the system significantly enhanced the detection of fabricated evidence, fostering high levels of confidence and trust among users. Moreover, this study delves into the legal and ethical considerations surrounding the deployment of deep fake-detection technologies, underscoring the necessity for legal frameworks to evolve in response to emerging digital threats. By addressing both the technical and jurisprudential challenges, this research contributes to safeguarding the evidential value of facial recognition in the judicial process against the disruptive potential of deepfake technologies.