Digital images have always played an important role in investigations. They can help confirm where someone was, what happened at a scene, who was present, or how an event unfolded.
But with the rise of AI generated images and deepfake content, digital evidence is becoming harder to assess at face value.
A photo may look convincing. A face may appear realistic. A scene may seem ordinary. But that does not always mean the image is reliable.
For investigators, forensic teams and government agencies, the question is no longer simply: is this image real or fake?
The stronger question is: can this image be trusted as evidence?

What makes deepfake evidence difficult?
A deepfake is not always a fully fake image. In many cases, only part of an image may be generated, edited or replaced.
A face may be altered. A background may be changed. An object may be added or removed. A person may appear to be in a place they were never actually in. This makes analysis more complex. Looking at the whole image is not always enough. Investigators may need to examine specific areas of the image, including faces, shadows, reflections, objects and background details.
The challenge is that AI generated content is improving quickly. Some images now appear natural to the human eye, especially after being compressed, resized or shared through social media platforms. That is why deepfake investigation needs a structured forensic workflow, not just a quick visual check.

Why AI detection tools are not enough on their own

AI detection tools can help flag suspicious images, especially when teams are reviewing large amounts of digital content.
However, these tools should not be used as the final answer.
A detection score alone does not explain how a conclusion was reached. In forensic work, findings need to be clear, reviewable and properly documented.
That is why suspected deepfake evidence should be assessed through a structured forensic process.

What should investigators look at?

A stronger approach to suspected deepfake evidence includes several layers of review.The first step is usually to preserve the original file where possible. Screenshots, forwarded images, and compressed downloads may remove important information. The original file can provide more detail about how the image was created, edited or stored.
Metadata should then be reviewed. This may include timestamps, device details, software tags, file history and other technical information. Missing metadata does not automatically prove manipulation, but it can raise questions when considered with other findings. Compression patterns can also be useful. If one part of an image has a different compression history to the rest of the file, it may suggest local editing or replacement. This is especially relevant when only part of the image is suspected of being altered.
The visual structure of the image should also be checked. Investigators may look at lighting, shadows, reflections, proportions and perspective. If a person’s shadow does not match the scene, or a reflection is missing where it should appear, that may indicate the image needs further review. Pixel level analysis can provide another layer of information. Differences in texture, sharpness, noise, blending or edge detail may help identify areas that do not match the rest of the image. These findings must be interpreted carefully, because compression and normal editing can also create visual artefacts.The aim is not to find one suspicious detail and make a conclusion. The aim is to see whether several findings point in the same direction.

The importance of clear documentation
Clear documentation is essential.
Investigators need to record what was examined, what tools or methods were used, what was found and what limitations were present.
This helps ensure the findings can be explained, reviewed and defended if required.

Why this matters for modern investigations
Deepfake technology creates new risks for law enforcement, forensic laboratories, border security, defence, legal teams and government agencies.
False or altered media can affect witness accounts, online investigations, identity verification, intelligence gathering and evidence review.
As the technology improves, investigation teams need to be prepared to assess digital media with care.
Relying only on appearance is no longer enough. Relying only on a detection score is also not enough.
The most reliable approach is a structured process that combines technical review, forensic judgement and clear reporting.

Moving from suspicion to evidence
Deepfake evidence should be treated with caution, but not with panic.
AI generated content can be investigated. Suspicious images can be reviewed. Key areas can be tested. Findings can be documented.
What matters is having the right process in place. Forensic image analysis should move beyond the question of whether a tool says an image is fake. It should focus on what can be observed, what can be tested and whether the findings can be clearly explained. As digital evidence continues to evolve, investigation teams will need forensic workflows that are practical, repeatable and defensible.
That is where careful analysis makes the difference.

Reference source: Forensic Focus, Deepfake Forensics: How to Analyze Suspected AI-Generated Images.