Research about the Ability of LLM in the Tamper-Detection Area
Xinyu Yang Jizhe Zhou
In recent years, particularly since the early 2020s, Large Language Models
(LLMs) have emerged as the most powerful AI tools in addressing a diverse range
of challenges, from natural language processing to complex problem-solving in
various domains. In the field of tamper detection, LLMs are capable of
identifying basic tampering activities.To assess the capabilities of LLMs in
more specialized domains, we have collected five different LLMs developed by
various companies: GPT-4, LLaMA, Bard, ERNIE Bot 4.0, and Tongyi Qianwen. This
diverse range of models allows for a comprehensive evaluation of their
performance in detecting sophisticated tampering instances.We devised two
domains of detection: AI-Generated Content (AIGC) detection and manipulation
detection. AIGC detection aims to test the ability to distinguish whether an
image is real or AI-generated. Manipulation detection, on the other hand,
focuses on identifying tampered images. According to our experiments, most LLMs
can identify composite pictures that are inconsistent with logic, and only more
powerful LLMs can distinguish logical, but visible signs of tampering to the
human eye. All of the LLMs can't identify carefully forged images and very
realistic images generated by AI. In the area of tamper detection, LLMs still
have a long way to go, particularly in reliably identifying highly
sophisticated forgeries and AI-generated images that closely mimic reality.