The Growing Need to Verify Digital Images in the AI Era

Not every photo (or video) is equal. There are many ways that a person could view an image: as either a genuine visual representation of a moment, event, or issue, or as a piece of artificial visual content created by AI.

While this trend isn’t limited to one platform or style of content, it’s evolving across the board. People continue to find value in visual content, but the way they evaluate visual content is changing. People don’t automatically trust the images they see online. They need to earn their trust.

From Documenting Real Events to Creating Visual Content Using AI

Historically, photography was all about documenting real events. When someone edited an image, it was usually obvious. With the rise of AI, we're seeing images of people and places that don't exist, but they look very realistic.

As such, browsing social media feeds today is like playing a game of "spot the difference," and it's becoming much harder to determine what is authentic and what has been created artificially.

Therefore, applications like an AI image detector are used increasingly to identify whether or not an image has been manipulated or created artificially.

Image: ChatGPT interface on mobile phone | Source: Pexels

Why Verifying Images is Becoming Part of Everyday Viewing

At one point in time, there was rarely ever a reason to question an image. Most people assumed that an image had some degree of basis in reality. That assumption is currently under scrutiny.

Images found in the news and media can influence how we understand stories. An image that misleads can completely alter the narrative of a story.

Additionally, people encounter images in messages and social media posts that appear legitimate but are false, and in some cases are intended to elicit an emotional response or support a specific viewpoint.

However, most importantly, since most fake images appear mixed in with real images, distinguishing between them is challenging. This has led to a greater sense of responsibility among viewers to be vigilant and to use their own judgment to verify information as part of the viewing experience.

How People Are Learning to Review Images

One of the most common techniques of reviewing images is reverse image search. Users are able to identify if an image has previously appeared elsewhere and in what other contexts. In doing so, the user can quickly identify whether an image is being misrepresented.

In addition, more people are paying close attention to the small details of images. Everyone appears to be more aware and observes more detail in images, such as strange lighting or minor distortions that may indicate that the image has been edited.

Users are also using AI to assist in reviewing for potential red flags and identifying potential patterns that could indicate a fake image.

Although neither technique is foolproof, collectively, they allow for a better educated way of consuming visual content.

Emerging Platforms and Their Role

As image verification becomes more important, platforms designed specifically to provide this service will become easier to access. What was once only accessible to specialists will soon be available to the average consumer.

Lenso.ai is a platform that addresses this growing need for accurate image verification. Lenso.ai provides users with the opportunity to review images and examine where they originated, enabling them to make more informed decisions about the visual content they consume. Rather than assuming, users can visually verify the details.

This represents a larger shift in behavior. Verification is no longer something additional. It is becoming a natural part of how people engage with digital content.

Image: Sample of Lenso.ai’s reverse image search result | Source: lenso.ai

The Challenge of Staying Ahead of AI in Detection

Detection of AI-generated images is not as simple as ever. AI-generated images are evolving to include less obvious AI indicators and more realistic detail.

Therefore, detection of AI-generated images is a constantly evolving target. Detection systems must evolve rapidly to stay ahead of improvements in AI generation systems.

Additionally, detection systems have limitations. Detection results are often ambiguous, and multiple detection systems may provide conflicting results. Therefore, detection typically requires verification by multiple methods.

Finally, awareness regarding the detection of AI-generated images is still uneven. Some users take an active interest in verifying images; however, many users form opinions based on initial perceptions, and this allows images to be disseminated further.

How to Effectively Verify Images

Verification of images generally begins with a brief internet search to determine if the image has been used previously and in what context. By doing so, users can determine if the image has been reused, taken out of context, or circulated for an extended period.

Based on the user's findings, the user will assess the credibility and familiarity of the source. Users who have determined that verification is necessary will use AI-based detection software to evaluate the image. Once evaluated, the user will cross-reference the findings with reporting from reputable sources to determine the complete picture.

When working within a team or organization, the process of verifying images is generally a systematic process. Prior to publishing or sharing any images, they are reviewed extensively.

During the review process, multiple detection systems are used to identify possible misrepresentations. Team members are instructed to scrutinize all images thoroughly and report any images that appear questionable. Teams maintain documentation of the verification processes, and this documentation provides consistency in the evaluation process.

When properly implemented, this methodology ensures that the images distributed by a team or organization are both effective and credible.