As the capabilities of artificial intelligence expand, so does its potential for misuse. While much attention has been paid to AI’s ability to generate misleading or false text, the growing threat posed by AI-generated images is rapidly catching up, and raising serious concerns among experts, journalists, and the general public.

A recent NBC News report highlighted the sharp rise in AI-generated images being used to deceive or misinform. From altered political photos circulated during election seasons to fake celebrity scandals, the proliferation of these synthetic visuals has made it increasingly difficult to separate fact from fiction in digital media.

Unlike traditional photoshopping or staged imagery, today’s AI tools can fabricate entirely new images with photorealistic precision. These visuals don’t just embellish or tweak, they invent. And without proper context or labeling, they often go viral on social media platforms, sowing confusion, division, and mistrust.

“AI image generation is like giving everyone a paintbrush—but without teaching them the difference between art and forgery,” says Brian Sathianathan, Co-Founder and CTO of Iterate.ai. “Misinformation spreads when innovation moves faster than integrity. The rise in AI-generated image misinformation highlights the urgent need for stronger verification tools and responsible AI development. As generative technology becomes more accessible, so does the risk of eroding public trust. If we want a future where people trust what they see, we have to build technology that earns that trust every step of the way.”

This growing mistrust isn’t just theoretical, it’s already playing out in real time. Earlier this year, a fake image of the Pope in a white puffer jacket sparked a media firestorm. Though harmless in intent, it was indistinguishable from a real photo at first glance, prompting thousands of shares and debates before its AI origins were confirmed. The potential for more harmful versions of this, like fake images of violence, natural disasters, or political figures, could have far more damaging consequences.

Part of what makes this issue so pressing is the speed and scale at which AI images can be produced. Tools like Midjourney, DALL·E, and Stable Diffusion can generate detailed visuals in seconds, requiring only a short text prompt. What once required technical skill and hours of editing can now be done by anyone with internet access.

Compounding the problem is the lack of clear guidelines or regulatory frameworks. In the U.S., there are currently no federal laws requiring AI-generated images to be labeled or disclosed. Some states, such as California and Texas, have introduced bills aimed at curbing the use of AI in political advertising or mandating transparency, but enforcement remains inconsistent and limited in scope.

Social media platforms, too, are struggling to keep up. While companies like Meta and X (formerly Twitter) have pledged to label AI-generated content and remove deepfakes that violate policy, the sheer volume of content makes moderation difficult. Many users still encounter manipulated visuals daily, often without knowing it.

Experts say the answer lies in a mix of technology, education, and policy. Emerging solutions like digital watermarking, cryptographic verification, and AI-detection tools are being explored to help users identify manipulated media. But these tools are not yet widely adopted or foolproof.

Ultimately, the public’s trust in visual information is at risk. Photographs and videos have historically been some of the most persuasive forms of evidence, but if AI continues to erode their credibility, society may enter a dangerous phase where “seeing is believing” no longer holds true.

With the 2026 U.S. midterm elections approaching and geopolitical tensions rising globally, the stakes are high. Whether it’s an image of a politician supposedly inciting violence, a doctored photo meant to provoke racial or religious hatred, or a fake news broadcast, AI-generated images have the potential to create real-world chaos if left unchecked.

The challenge now is not only how to detect these images, but how to preserve the public’s ability to trust what they see online. As Sathianathan emphasizes, the future of visual truth depends on it.