CogniCred: A Dataset and Benchmark for Cognitive Credential Forgery Detection

Credentials, such as corporate qualifications and personalidentification, serve as common forms of evidentiary materials. The forgeryof such credentials poses significant risks to security, making its detectiona long-standing area of widespread concern. With the advancement of AI-Generated Content technologies, forgery techniques can now largely pre-serve visual characteristics, making purely vision-based detection meth-ods insufficient to meet practical demands. Instead, a growing number offorgeries necessitate cognitive reasoning for detection. While the devel-opment of Multimodal Large Language Models (MLLMs) presents newopportunities for complex credential forgery detection, the scarcity ofdatasets and benchmarks severely impedes progress in this critical do-main. To bridge this gap, we introduce CogniCred — the first large-scale,multimodal dataset built upon complex, real-world credentials with care-fully crafted cognitive-level forgeries. Along with this dataset, we proposea dedicated benchmark, namely CogniCredBench, that evaluates modelperformance across five distinct types of cognitive forgeries. We conductcomprehensive evaluations across 21 mainstream MLLMs, revealing sub-stantial performance disparities and a strong bias toward text-based rea-soning, highlighting that current models still fall short in balanced multi-modal reasoning for cognitive forgery detection. Our datasets are publiclyavailable at https://huggingface.co/datasets/sjs0606/CogniCred.