Traditional forensic methods for authenticating digital evidence—such as examining metadata, pixel inconsistencies, or compression artifacts—are increasingly insufficient against high-quality deepfakes. As Maras and Alexandrou (2019) note, forensic experts once relied on visible flaws like mismatched lighting or irregular frame rates, but newer generative adversarial networks are capable of producing videos with few, if any, detectable anomalies. This sophistication undermines the ability of courts to rely on expert testimony or conventional tools to distinguish between authentic and manipulated files. The problem is compounded by the fact that many detection systems are reactive rather than proactive. According to the 2024 IEEE Access study, AI detection tools often require access to the original source file and extensive training data in order to identify subtle discrepancies. In practice, however, courts may only have access to circulated or compressed versions of a video, which reduces the effectiveness of these forensic methods. This lag in detection capabilities illustrates what Brundage et al. (2018) describe as the "security arms race" between creators and detectors of malicious AI.