In a 2024 custody dispute in a family court in the UK, one party submitted audio recordings purporting to capture the other parent making threatening statements. The recordings were convincing. The voice matched. The emotional cadence was natural. But forensic analysis revealed that the audio had been generated using a commercially available AI voice cloning tool. The threatening statements never happened. They were manufactured from a few minutes of sample audio scraped from social media.
This is not an isolated incident. I have been retained as an expert in multiple matters over the past two years involving AI-generated or AI-manipulated evidence, including synthetic audio, fabricated photographs, and altered video. The technology has crossed a threshold where creating convincing fake evidence requires no technical sophistication at all. A teenager with a smartphone can produce synthetic media that would have required a Hollywood studio five years ago.
The State of Synthetic Media Technology
Understanding the litigation risk requires understanding what the technology can do today, not what it could do three years ago when most judges and attorneys last updated their mental model.
AI-generated images produced by models like Midjourney, DALL-E 3, and Stable Diffusion can create photorealistic scenes that never occurred. Earlier generations of these tools left detectable artifacts: warped hands, inconsistent lighting, text that did not quite make sense. Current models have largely eliminated these tells. A generated photograph of a car accident, a workplace condition, or a person at a specific location can now be virtually indistinguishable from an authentic photograph to the naked eye.
Voice cloning has become remarkably accessible. Services like ElevenLabs can create a convincing voice clone from as little as thirty seconds of sample audio. The cloned voice can then be used to generate any statement in real time. The implications for audio evidence in litigation are profound. Phone recordings, voicemails, and recorded conversations can all be fabricated or altered with minimal effort.
Video deepfakes have progressed from obviously artificial face-swaps to increasingly convincing full-body synthesis. While real-time deepfake video still has detectable artifacts under close examination, pre-produced deepfake video, where the creator has time to refine the output, can be extremely difficult to identify without forensic tools.
We are entering a world where the phrase "seeing is believing" is no longer operationally true. For a legal system that has relied on photographic and audio evidence as near-conclusive proof for over a century, this is a foundational crisis.
Forensic Detection Methodology
When I conduct forensic analysis of suspected synthetic media, I employ multiple complementary detection approaches. No single method is reliable on its own, but the combination provides a robust assessment.
Metadata analysis is the first line of investigation. Authentic photographs and videos contain EXIF data that records the device, timestamp, GPS coordinates, and camera settings. AI-generated images typically lack this metadata entirely or contain metadata that is internally inconsistent. However, metadata can be spoofed, so its presence does not confirm authenticity, and its absence does not confirm fabrication.
Statistical analysis examines the pixel-level characteristics of the image or video. Generative AI models produce images with statistical properties that differ subtly from photographs captured by camera sensors. These differences include variations in noise patterns, frequency domain characteristics, and color channel correlations. Tools based on these methods can detect synthetic content with high accuracy, though detection rates vary by generator model and post-processing.
Physiological analysis is particularly useful for video. Real human faces exhibit micro-expressions, natural blinking patterns, and subtle skin texture changes that are difficult for generative models to reproduce perfectly. Analysis of eye reflection consistency, where reflections in both eyes should match the same environment, has proven particularly effective at detecting face-swap deepfakes.
Provenance verification examines the chain of custody for the digital file. Content authenticity standards like C2PA (Coalition for Content Provenance and Authenticity) embed cryptographic signatures at the point of capture, creating an unbroken chain from camera sensor to courtroom. However, most existing evidence lacks provenance metadata, and the standard is not yet widely adopted.
The Arms Race Problem
It is important to be candid about the limitations of detection. Forensic detection and synthetic generation are locked in an adversarial arms race. Each improvement in detection capability drives improvements in generation quality. Detection methods that are effective today may be circumvented by next-generation models. Any expert who claims that synthetic media can always be reliably detected is overstating the current science.
What I can say with confidence is that current forensic methods, when applied in combination by experienced practitioners, can detect the vast majority of synthetic media produced by commercially available tools. The more difficult cases involve state-level actors or highly sophisticated adversaries with access to custom models and the resources to iteratively refine their output against detection tools. In typical civil and criminal litigation, the synthetic media encountered is produced by consumer-grade tools and remains detectable.
Evidentiary Challenges and the Authentication Framework
The Federal Rules of Evidence require that evidence be authenticated before admission. Rule 901(a) states that the proponent must produce evidence sufficient to support a finding that the item is what the proponent claims it is. Historically, for photographs and recordings, this was satisfied by testimony from a witness with knowledge: someone who was there, who recognizes the scene, who can confirm the recording is accurate.
Synthetic media breaks this framework in two directions. First, fabricated evidence can be offered with false testimony from a witness who claims to authenticate it. Second, and perhaps more insidiously, authentic evidence can be challenged as fabricated. This is what researchers call the "liar's dividend": the mere existence of deepfake technology gives any party grounds to challenge the authenticity of any media evidence, even when it is genuine.
Several courts have begun to grapple with this challenge. In 2024, a federal judge in the Southern District of New York required parties to disclose whether any evidence had been AI-generated or AI-modified. Some state courts have begun requiring expert authentication for digital media in contested cases. These are important first steps, but the rules have not yet caught up with the technology.
Practical Recommendations for Attorneys
Preserve metadata aggressively. When your client captures photos, videos, or audio that may become evidence, preserve the original files with full metadata intact. Do not screenshot, re-encode, or compress. The original file is exponentially more useful for forensic analysis than a derivative copy.
Challenge early. If you suspect opposing evidence may be synthetic, raise the issue at the earliest opportunity. Request the original digital files, not printouts or compressed copies. Request metadata, device information, and chain of custody documentation. Retain a forensic expert before depositions so you can ask technically informed questions about how the evidence was captured and stored.
Anticipate the defense. If your evidence is genuine, prepare for the possibility that opposing counsel will claim it is fabricated. Proactive forensic authentication of your own evidence, conducted before trial, eliminates this line of attack and demonstrates confidence in the evidence's integrity.
The synthetic evidence problem will only intensify as generative AI continues to improve. Courts, attorneys, and expert witnesses need to develop robust frameworks now, before the next generation of tools makes detection even more challenging. The integrity of the evidentiary system depends on it.
The Criterion AI provides expert witness services and litigation support for matters involving artificial intelligence, machine learning, and algorithmic decision-making. For a confidential consultation on an active or anticipated matter, contact us at criterion@thecriterionai.com or call (617) 798-9715.