Deepfakes and Synthetic Media: The Growing Challenge for Digital Evidence

Digital evidence has long carried an implicit assumption of reliability. A photograph shows what happened. A voice recording captures what was said. A video records what took place. Courts have historically treated this type of evidence as broadly trustworthy, subject to standard questions of provenance and chain of custody.

That assumption is under increasing pressure. Advances in artificial intelligence, and in particular generative AI models, have made it possible to create synthetic media (commonly referred to as deepfakes) that is difficult to distinguish from authentic recordings. Faces can be swapped in video. Voices can be cloned from short audio samples. Images can be generated or altered in ways that leave no obvious visual trace of manipulation.

For solicitors and counsel handling disputes where audio, video, or image evidence is material, this development raises practical questions that did not exist a few years ago. This article sets out the current state of the technology, the methods available for detection, and the considerations that apply when authenticity is challenged.

What deepfakes are, technically

The term “deepfake” refers to synthetic media generated or manipulated using deep learning techniques. The most common forms include:

Face swapping: Replacing one person’s face with another in video footage, using neural networks trained on images of both individuals. The output retains the original person’s body movements and expressions while rendering a different face.
Voice cloning: Generating synthetic speech that mimics a specific individual’s voice, tone, and cadence. Current models can produce convincing results from relatively short reference samples, in some cases as little as a few seconds of audio.
Face re-enactment: Manipulating an existing video so that a person appears to say or do something they did not, by transferring facial movements from a source performer to the target.
Generative image creation: Producing entirely synthetic photographs of people, objects, or scenes using models such as diffusion-based architectures. These images depict events that never occurred and people who may not exist.

The quality of synthetic media has improved considerably in recent years, though the degree varies across different types of media and generation methods. Early deepfakes often exhibited visible artefacts (unnatural blinking, inconsistent lighting, blurred edges around the face) that made detection relatively straightforward. Current generation tools produce output that can be difficult for a human observer to identify as synthetic, particularly when the media is compressed, low resolution, or viewed briefly.

Why this matters for litigation

The relevance of deepfake technology to litigation is twofold.

First, there is the direct risk that fabricated evidence is presented as authentic. A party could submit a synthetic audio recording of a conversation that did not take place, or a manipulated video that misrepresents events. While this has always been possible in theory (analogue recordings could be edited, photographs could be doctored) the barrier to producing convincing fabrications has fallen significantly. Tools that generate synthetic media are increasingly accessible, and many do not require specialist technical knowledge to produce basic output, though the quality of the result varies.

Second, and in some respects more immediately significant, is the indirect effect on the weight of legitimate evidence. Once it becomes known that convincing fabrications are possible, the authenticity of genuine recordings can be challenged more readily. A party facing damaging audio or video evidence may argue that it could have been fabricated, shifting the burden to the other side to demonstrate authenticity through forensic analysis. This has been described in academic literature as the “liar’s dividend”: the ability to cast doubt on real evidence by pointing to the existence of deepfake technology.

Both risks may be relevant across a range of proceedings, depending on the facts, including for example commercial disputes where recorded conversations are relied upon, employment matters where video or audio evidence of workplace conduct is in play, family proceedings where covert recordings feature, and fraud cases where the identity of a participant is contested.

Detection: what forensic analysis can reveal

Forensic detection of synthetic media is an active area of research and practice. The methods available fall into several broad categories:

Pixel-level analysis. Examination of the image or video at the pixel level can reveal artefacts introduced by generative models. These may include inconsistencies in noise patterns (the subtle random variation present in all photographs taken by physical cameras), compression artefacts that differ from those produced by standard codecs, and geometric inconsistencies in facial features or lighting. Generative models do not perfectly replicate the physics of image capture, and forensic tools can detect statistical signatures that distinguish synthetic content from camera-captured content.

Temporal analysis. In video, frame-by-frame analysis can identify inconsistencies across time. Deepfake face swaps may produce subtle flickering, inconsistent head pose tracking, or unnatural transitions between frames. Audio deepfakes may exhibit discontinuities in pitch, breathing patterns, or background noise that are not consistent with continuous recording.

Metadata examination. Authentic digital media typically carries metadata (EXIF data for images, container metadata for video, header information for audio) that records the device, settings, and circumstances of capture. Synthetic media may lack this metadata entirely, or carry metadata that is inconsistent with the claimed provenance. The absence of expected metadata is not conclusive (metadata can be stripped legitimately, and some platforms remove it on upload) but its presence or absence is part of the overall assessment.

Provenance and chain of custody. Beyond the media itself, forensic analysis considers how the recording was obtained, stored, and transmitted. A video extracted forensically from a device with an intact chain of custody is substantially more reliable than one received as an email attachment from an unknown source. The circumstances surrounding the evidence are as important as the technical analysis of the content.

AI-based detection models. Specialised neural networks have been developed to classify media as authentic or synthetic. These models are trained on large datasets of both real and fabricated content and can identify patterns that are not visible to human observers. However, detection models are engaged in an ongoing adversarial dynamic with generation models: as detection improves, generation techniques evolve to evade detection. The reliability of any given detection model depends on the generation method used, the quality of the synthetic media, and how current the detection model is.

It is important to note that no single detection method is definitive. Forensic assessment of media authenticity involves applying multiple techniques and evaluating the results in combination. A finding that media is “consistent with authentic capture” or “exhibits characteristics of synthetic generation” is a probabilistic assessment, not a binary determination. As with all expert evidence under CPR Part 35, the expert’s overriding duty is to the court. That duty requires the expert to explain the basis for their assessment, its limitations, and the degree of confidence it supports, regardless of which party has instructed them.

The authentication challenge for courts

The procedural framework for authenticating digital evidence in England and Wales does not yet specifically address deepfakes. The general principles of evidence law apply: real evidence must be authenticated, and the court must be satisfied that it is what it purports to be. The standard directions for electronic disclosure under Practice Direction 31B and the principles in cases concerning the admissibility of electronic evidence provide the current framework.

In practice, where the authenticity of audio, video, or image evidence is challenged, the court is likely to consider the provenance of the evidence (how it was obtained and by whom), the chain of custody (how it was preserved and transmitted), any forensic analysis of the media itself, and the surrounding factual context (whether the content is consistent with other evidence in the case).

Technology expert evidence can assist the court in each of these areas. The expert can examine the media at a technical level, assess whether it is consistent with authentic capture or exhibits indicators of manipulation, and explain the significance of the findings in terms the court can follow. In my experience, it is important for expert evidence on media authenticity to address not only whether manipulation can be detected, but also what the limitations of the analysis are and what alternative explanations exist.

Practical guidance for solicitors

If audio, video, or image evidence is likely to feature in your case, and there is any prospect that authenticity may be challenged, the following steps are worth considering:

Preserve the original. Ensure the earliest available version of the media is preserved, ideally the file as it exists on the device that captured it, before it has been compressed, converted, or transmitted through messaging platforms. Each generation of copying or compression degrades the forensic information available for analysis.
Maintain chain of custody. Document how the evidence was obtained, from whom, and how it has been stored. A clear chain of custody strengthens the evidential foundation and makes forensic analysis more meaningful.
Do not rely on screenshots or screen recordings of media. A screenshot of a video, or a screen recording of a playback, strips away the metadata and technical detail that forensic analysis depends on. Where possible, obtain the original file.
Instruct a forensic expert early. If authenticity is likely to be contested, early expert involvement allows for proper preservation and a thorough analysis before positions are fixed. An expert can also advise on what additional evidence (device data, platform records, corroborating metadata) may be available to support or challenge authenticity.
Consider the “liar’s dividend” risk proactively. If you hold genuine evidence that is likely to be challenged as fabricated, anticipate the challenge and prepare forensic support for its authenticity before it is raised. A proactive approach is more persuasive than a reactive one.
Be realistic about the limits of detection. Forensic analysis can identify many forms of synthetic media, but detection is not infallible. Where expert evidence is equivocal, the surrounding factual context, including consistency with other evidence in the case, may be as important as the technical findings.

Looking ahead

The technology underlying synthetic media continues to advance. Generation models are becoming more capable, producing output that is increasingly difficult to distinguish from authentic content. At the same time, detection methods are improving, and there is growing interest in provenance standards (such as the C2PA content authenticity framework) that embed cryptographic proof of origin into media at the point of capture.

For the present, the practical reality is that the authenticity of digital media can no longer be taken for granted. Where audio, video, or image evidence is material to a dispute, solicitors and counsel should consider the possibility that its authenticity may be challenged, and ensure that the evidential foundation, including forensic analysis where appropriate, is sufficient to address that challenge.

In my experience, the courts are capable of evaluating contested evidence when it is properly presented and supported by appropriate expert analysis. The role of the technology expert is to provide the technical analysis that enables the court to make that evaluation on an informed basis.