The "Truth" in Court: "Is That Evidence AI?" The Court-Shaking Fake Images and the Limits of Technology to Detect Them

"Is That Evidence AI-Generated?" Fake Images Shaking the Courtroom and the Limits of Detection Technology

Recordings left on smartphones, footage from security cameras, and photos posted on social media. When trouble arises, these records can serve as clues to untangle conflicting accounts.

But what if someone points out that these records might have been generated by AI?

Does a natural appearance mean it's genuine? If a detection tool raises suspicion, is it fake? Do you even still have the original files of photos posted years ago?

Research in Canada highlights the complexity of this issue. Technology to detect AI-generated evidence is being developed. However, the success achieved in laboratories cannot be directly applied to real-world judicial proceedings.

Even with Multiple Detection AIs, No Conclusion Can Be Reached

According to an article by the Canadian Press published by Global News on October 30, 2026, the research is co-led by Maura Grossman, a computer science researcher at the University of Waterloo, who also teaches at Osgoode Hall Law School at York University.

The team's goal is to create a free, user-friendly open-source tool for use by courts and those involved in the judiciary. It is intended to assist in examining whether images and other media have been created or altered by AI.

The system's feature is that it doesn't rely on a single algorithm for judgment but combines multiple advanced detection algorithms. Each algorithm makes its determination, and the results are pooled together like a committee.

However, as the two-year research plan nears its midpoint, challenges have emerged.

In two real-world trials conducted by Grossman, the algorithms' judgments did not align sufficiently to produce useful conclusions.

It's important to note that this report does not prove that all AI detection technologies are unusable. The original article does not provide detailed conditions of these two cases or accuracy rates based on large-scale evaluations. It also doesn't state that the verification was formally adopted in court.

Nevertheless, when faced with real-world materials, even the latest technology combinations can result in divided judgments. This fact challenges the expectation that "AI fakes can be resolved by showing them to another AI."

Once Posted on Social Media, Clues for Verification Diminish

Why is there a gap between the laboratory and reality?

One reason Grossman cites is that images change during the distribution process.

The file immediately after shooting and the image posted on social media are not necessarily in the same state. They may be compressed upon posting, and metadata, which is ancillary information about the shooting, may be removed.

Furthermore, the image may be saved, posted elsewhere, and shared as a screenshot. Even if the visual pattern appears similar, the information that can be investigated changes from the original file.

Moreover, the issue may not arise immediately after posting. By the time a dispute occurs years later, the original file might have disappeared.

It's crucial to note that compression or the absence of metadata itself does not constitute evidence of fabrication. Such states can occur simply by using social media normally.

Even genuine images can become scarce in terms of verification materials. Therefore, "unable to determine" and "was fake" must be clearly distinguished.

In Court, "Judgment Results" Alone Are Not Enough

In everyday social media browsing, it might suffice to decide "not to spread because it's suspicious."

However, in court, the impact of the judgment is far greater. Whether to believe a certain record can affect a person's responsibility and life.

Grossman emphasizes from the outset that the tool is not considered an all-encompassing solution and that human involvement and discernment are necessary. Someone who can explain why such a judgment was made is also needed.

If detection results are to be used, what was investigated, under what conditions judgment becomes difficult, and how much uncertainty remains must be addressed.

Simply saying "AI said it was fake" does not suffice as thorough verification of evidence that might have been created by another AI.

This research questions not only the performance of detectors but also whether detection results can be understood and positioned within the judiciary in a form that people can examine.

The Reason for Aiming for a Free Tool Lies in the Cost of Verification

The University of Waterloo's research introduction explains that having experts examine AI-related evidence incurs costs and can lead to trial delays, posing challenges for cases other than large-scale ones.

The aim of a free and user-friendly tool is also to assist those who face lawsuits alone or cannot access sufficient professional support.

This raises the danger that the burden of creating fakes and verifying them becomes unbalanced.

What if every suspicious image requires expensive appraisal? Conversely, what if those who submit genuine records bear significant time and cost to explain their authenticity?

This is a fairness issue that can be considered from the research's background. How widely can means to verify evidence be made available? Along with detection accuracy, these conditions are also being questioned.

On Social Media, It's Not Just the Fear of Fakes Getting Through

In public discussions related to this issue, the Reddit UK police-related community "r/policeuk" has been discussing the possibility of AI-generated or altered images and videos being presented as evidence.

The following is not a direct response to the current Global News article but a summary of posts in another thread dealing with the same theme. These are anonymous posts, and the authenticity of the posters' professions or experiences has not been independently verified.

One concern is that doubts about whether security footage, for example, was AI-generated could undermine the credibility of genuine records.

On the other hand, there are opinions that obtaining data directly from the source of the recording and cross-referencing multiple videos of the same event will become more important.

There are also posts supporting the use of AI as an aid for initial verification, followed by human investigation, noting that test data for detection tools and videos routinely handled in the field are not the same.

These do not represent the entire public opinion. However, it is clear that interest is directed not only at "can fakes be detected" but also at "how to maintain trust in genuine items."

Even Suspicions of "This Is AI" Require Verification

The issues brought about by generative AI have two directions.

One is that fabrications are accepted as genuine. The other is that genuine items are dismissed as fabrications.

If we only guard against the former and start doubting all footage, the efforts of those who record incidents or events could become meaningless.

Conversely, if we trust based solely on natural appearance, we risk accepting cleverly crafted materials.

What is needed is not to decide in advance whether to "believe" or "doubt," but to verify the origins of the record.

Who recorded it, when, and where? Is the original data available? How was it shared? Does it contradict other materials?

Those pointing out the possibility of AI manipulation also need materials that allow for examination of their claims. Just because there is a possibility does not mean each record has become fake.

"How to Preserve Records" Concerns Us in Japan, Too

This report is about research in Canada and does not imply that Japanese courts are adopting the same system.

Nevertheless, there are many points of contact with everyday life.

Dashcams for traffic accidents, work-related communications, disputes with stores, harassment on social media. Many people likely keep photos, recordings, and screen captures for when something happens.

As a suggestion for everyday record management from this issue, it is important to keep not only shared copies but also the data in its acquired state.

Even if you have cropped images for better visibility, save the originals before cropping. Even if you create short videos, keep the original videos including the before and after. Ensure that the date and circumstances of the recording can be traced later.

These do not guarantee that specific materials will be accepted as evidence in Japanese courts. However, they serve as a way to avoid reducing clues that can be confirmed later.

"How the record was preserved" becomes as important as "what is depicted."

Enhancing Image Quality Is Different from Verifying Facts

Another thing to be cautious about in daily life is image correction using generative AI.

Here, we consider issues in handling records, separate from the two trials conducted by Grossman and her team.

Brightening a dark video and having generative AI fill in unseen parts do not mean the same thing. Even if plausible faces or object shapes appear, it does not confirm their existence in the original scene.

Enhanced images leave a strong impression on viewers. However, the vividness of the impression is different from the certainty of the record.

Even when creating processed versions for confirmation or explanation, it is important not to confuse them with the original data. Where the record ends and processing begins must be clearly distinguished. Losing this distinction can complicate judgment, even with well-intentioned work.

Waiting for a "Universal Detector" Is Not Enough

Grossman and her team continue their research and plan to gather opinions from law enforcement and policymakers in the next phase.

She highlights not only technology but also laws, regulations, policies, and education for people to discern information.

Is it acceptable to concentrate the burden on the side detecting harmful creations after they are made? Should there be prohibitions on creating certain things in the first place? The original article also touches on these policy considerations. However, no specific proposals for what to prohibit and to what extent have been presented.

The difficulties in research are not reasons to give up. Rather, they provide materials to discern what technology can do and what cannot be solely shouldered by technology.

The trust in future evidence cannot be supported by the answer from a single detector alone. It requires building up original data, the history of the record, surrounding materials, and human explanations.

The value of photos and videos is not disappearing. To preserve that value, it is now an era where we must verify not only the content depicted but also the path that led to it.

Sources and References

1. Global News / The Canadian Press:
   Grossman's explanation, the mechanism combining multiple detection algorithms, the insufficient agreement in judgments in two actual cases, the issues of image compression and metadata loss, and future research directions.
   https://globalnews.ca/news/12127820/distinguishing-ai-information-in-court/

2. University of Waterloo: Background and Purpose of the Research Project
   Research system supported by CIFAR, reasons for aiming for a free and open-source tool, explanations regarding the cost of expert verification and access to justice.University of Waterloo
   https://uwaterloo.ca/computer-science/news/cs-professors-receive-new-cifar-grants-address-ai-safety

3. Reddit "r/policeuk": Public Discussion on AI-Generated and Altered Evidence
   Concerns about even genuine security footage being doubted, references to verifying the source of recordings and cross-referencing multiple materials, and posts on the division of roles between humans and AI. Introduced as related discussions, not direct responses to the original article.reddit.com
   https://www.reddit.com/r/policeuk/comments/1r5bxef/has_anyone_come_across_an_instance_of_ai/