Meta logo. Daniel Cole/Reuters A new AI-generated content detection tool from Meta, introduced this week alongside its Muse Image imaging model, failed to identify some of the images created by the technology itself after they were cropped, according to a Reuters analysis. The conclusion highlights the challenges of verifying AI-generated images after common edits, a limitation that could make it difficult to identify deepfakes on the internet during an intense electoral period in the United States. ??Do you have any reporting suggestions? Send to g1 In an analysis of 40 images generated with Muse Image, Reuters found that the tool correctly identified all original versions created by AI. However, it failed to recognize 55% of these images after they were cropped to about a third or half of their original size. On its website, Meta claims that the preliminary version of the tool can identify images generated by its AI models even after cropping, thanks to an invisible watermarking system called Content Seal, incorporated into all images produced by Muse Image.
Meta tool stops recognizing AI images after cropping, says agency
Meta logo. Daniel Cole/Reuters A new AI-generated content detection tool from Meta, introduced this week alongside its Muse Image imaging model, failed to identify some of the images created by the technology itself...
The feature was developed to help users check whether an image was created by the company's artificial intelligence. Asked about the results of the Reuters analysis, Meta highlighted that the tool is still in the preview phase. The company said the watermark was designed to withstand common edits, but that the signal may be lost when an image undergoes more severe cropping. Competitors Google and OpenAI have also warned that their detection tools are not capable of identifying all forms of image manipulation. In March, Meta's Oversight Board — an independent body of experts that makes binding decisions and recommendations about content on the company's platforms — called on the company to expand its efforts to combat the "proliferation of misleading AI-generated content." The group also defended investments in more robust detection tools. Siwei Lyu, professor of computer science at the State University of New York at Buffalo and researcher in the field of forensic analysis of AI-generated images, stated that he had not evaluated Meta's tool, but highlighted that systems based on watermarks have limitations. "Watermark-based methods can be highly effective when the signal remains intact. However, any modifications that remove or weaken it — such as cropping, resizing, heavy compression, or other editing — can reduce their effectiveness, depending on how the watermark was developed," Lyu said. Sarah Barrington, an AI researcher and doctoral candidate at the University of California, Berkeley (UC Berkeley) School of Information, said watermarking technology holds promise for the future of AI-generated content, although it has limitations. "Like many digital or physical security measures, this technology may not be completely foolproof. Still, even if it only detects 90% of cases, this represents a significant advance compared to having no identification mechanism," she said.
